Journal articles: 'In silico drug prediction'

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Dmitriev, Alexander V., Anastassia V. Rudik, Dmitry A. Karasev, Pavel V. Pogodin, Alexey A. Lagunin, Dmitry A. Filimonov, and Vladimir V. Poroikov. "In Silico Prediction of Drug–Drug Interactions Mediated by Cytochrome P450 Isoforms." Pharmaceutics 13, no. 4 (April 13, 2021): 538. http://dx.doi.org/10.3390/pharmaceutics13040538.

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Drug–drug interactions (DDIs) can cause drug toxicities, reduced pharmacological effects, and adverse drug reactions. Studies aiming to determine the possible DDIs for an investigational drug are part of the drug discovery and development process and include an assessment of the DDIs potential mediated by inhibition or induction of the most important drug-metabolizing cytochrome P450 isoforms. Our study was dedicated to creating a computer model for prediction of the DDIs mediated by the seven most important P450 cytochromes: CYP1A2, CYP2B6, CYP2C19, CYP2C8, CYP2C9, CYP2D6, and CYP3A4. For the creation of structure–activity relationship (SAR) models that predict metabolism-mediated DDIs for pairs of molecules, we applied the Prediction of Activity Spectra for Substances (PASS) software and Pairs of Substances Multilevel Neighborhoods of Atoms (PoSMNA) descriptors calculated based on structural formulas. About 2500 records on DDIs mediated by these cytochromes were used as a training set. Prediction can be carried out both for known drugs and for new, not-yet-synthesized substances. The average accuracy of the prediction of DDIs mediated by various isoforms of cytochrome P450 estimated by leave-one-out cross-validation (LOO CV) procedures was about 0.92. The SAR models created are publicly available as a web resource and provide predictions of DDIs mediated by the most important cytochromes P450.

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Hutter, M. "In Silico Prediction of Drug Properties." Current Medicinal Chemistry 16, no. 2 (January 1, 2009): 189–202. http://dx.doi.org/10.2174/092986709787002736.

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Ghislat, Ghita, Taufiq Rahman, and Pedro J. Ballester. "Identification and Validation of Carbonic Anhydrase II as the First Target of the Anti-Inflammatory Drug Actarit." Biomolecules 10, no. 11 (November 19, 2020): 1570. http://dx.doi.org/10.3390/biom10111570.

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Background and purpose: Identifying the macromolecular targets of drug molecules is a fundamental aspect of drug discovery and pharmacology. Several drugs remain without known targets (orphan) despite large-scale in silico and in vitro target prediction efforts. Ligand-centric chemical-similarity-based methods for in silico target prediction have been found to be particularly powerful, but the question remains of whether they are able to discover targets for target-orphan drugs. Experimental Approach: We used one of these in silico methods to carry out a target prediction analysis for two orphan drugs: actarit and malotilate. The top target predicted for each drug was carbonic anhydrase II (CAII). Each drug was therefore quantitatively evaluated for CAII inhibition to validate these two prospective predictions. Key Results: Actarit showed in vitro concentration-dependent inhibition of CAII activity with submicromolar potency (IC50 = 422 nM) whilst no consistent inhibition was observed for malotilate. Among the other 25 targets predicted for actarit, RORγ (RAR-related orphan receptor-gamma) is promising in that it is strongly related to actarit’s indication, rheumatoid arthritis (RA). Conclusion and Implications: This study is a proof-of-concept of the utility of MolTarPred for the fast and cost-effective identification of targets of orphan drugs. Furthermore, the mechanism of action of actarit as an anti-RA agent can now be re-examined from a CAII-inhibitor perspective, given existing relationships between this target and RA. Moreover, the confirmed CAII-actarit association supports investigating the repositioning of actarit on other CAII-linked indications (e.g., hypertension, epilepsy, migraine, anemia and bone, eye and cardiac disorders).

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Paranjpe, Pankaj V., George M. Grass, and Patrick J. Sinko. "In Silico Tools for Drug Absorption Prediction." American Journal of Drug Delivery 1, no. 2 (2003): 133–48. http://dx.doi.org/10.2165/00137696-200301020-00005.

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Carbonell, Pablo, and Jean-Yves Trosset. "Overcoming drug resistance through in silico prediction." Drug Discovery Today: Technologies 11 (March 2014): 101–7. http://dx.doi.org/10.1016/j.ddtec.2014.03.012.

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Dmitriev, Alexander V., Alexey A. Lagunin, Dmitry А. Karasev, Anastasia V. Rudik, Pavel V. Pogodin, Dmitry A. Filimonov, and Vladimir V. Poroikov. "Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes." Current Topics in Medicinal Chemistry 19, no. 5 (April 18, 2019): 319–36. http://dx.doi.org/10.2174/1568026619666190123160406.

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Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.

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Sharma, S., K. Daniel, V. Daniel, and L. Sharma. "IN-SILICO PRELIMINARY DOCKING SCREENING OF SOME ANTI-ALZHEIMER DRUGS." INDIAN DRUGS 53, no. 06 (June 28, 2016): 74–79. http://dx.doi.org/10.53879/id.53.06.10429.

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Alzheimer’s disease is an irreversible, progressive brain disease that slowly destroys cognition function. It is neurodegenerative disease & most common kind of dementia. The main purpose of this work is to perform preliminary docking screening & estimate toxic properties of some anti-Alzheimer's drugs through computational software. To assess toxic properties of some anti-Alzheimer’s drugs, through Lipinski rule of five. Drug-likeness and toxic properties of selective drugs were determined by employing Osiris server. To calculate the biological activity spectrum through prediction of activity spectra for a drug which provide intrinsic property that correspond to different pharmacological effects, physiological and biochemical mechanisms of action. The OSIRIS toxicity predictions resulted for toxicity, cLogP value, drug likeness and drug-score of each molecular imprint. These findings are relevant for the exploration of drug action of any compound of Anti- Alzheimer’s drug using both animal models and in silico strategies.

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Dewulf, Pieter, Michiel Stock, and Bernard De Baets. "Cold-Start Problems in Data-Driven Prediction of Drug–Drug Interaction Effects." Pharmaceuticals 14, no. 5 (May 2, 2021): 429. http://dx.doi.org/10.3390/ph14050429.

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Combining drugs, a phenomenon often referred to as polypharmacy, can induce additional adverse effects. The identification of adverse combinations is a key task in pharmacovigilance. In this context, in silico approaches based on machine learning are promising as they can learn from a limited number of combinations to predict for all. In this work, we identify various subtasks in predicting effects caused by drug–drug interaction. Predicting drug–drug interaction effects for drugs that already exist is very different from predicting outcomes for newly developed drugs, commonly called a cold-start problem. We propose suitable validation schemes for the different subtasks that emerge. These validation schemes are critical to correctly assess the performance. We develop a new model that obtains AUC-ROC =0.843 for the hardest cold-start task up to AUC-ROC =0.957 for the easiest one on the benchmark dataset of Zitnik et al. Finally, we illustrate how our predictions can be used to improve post-market surveillance systems or detect drug–drug interaction effects earlier during drug development.

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Wang, Xiao, Chen, and Wang. "In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method." International Journal of Molecular Sciences 20, no. 17 (August 22, 2019): 4106. http://dx.doi.org/10.3390/ijms20174106.

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Drug-induced liver injury (DILI) is a major factor in the development of drugs and the safety of drugs. If the DILI cannot be effectively predicted during the development of the drug, it will cause the drug to be withdrawn from markets. Therefore, DILI is crucial at the early stages of drug research. This work presents a 2-class ensemble classifier model for predicting DILI, with 2D molecular descriptors and fingerprints on a dataset of 450 compounds. The purpose of our study is to investigate which are the key molecular fingerprints that may cause DILI risk, and then to obtain a reliable ensemble model to predict DILI risk with these key factors. Experimental results suggested that 8 molecular fingerprints are very critical for predicting DILI, and also obtained the best ratio of molecular fingerprints to molecular descriptors. The result of the 5-fold cross-validation of the ensemble vote classifier method obtain an accuracy of 77.25%, and the accuracy of the test set was 81.67%. This model could be used for drug‐induced liver injury prediction.

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Andrade, Carolina, Diego Silva, and Rodolpho Braga. "In silico Prediction of Drug Metabolism by P450." Current Drug Metabolism 15, no. 5 (November 26, 2014): 514–25. http://dx.doi.org/10.2174/1389200215666140908102530.

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Yao, Xin-Qiu, Shashank Jariwala, and Barry J. Grant. "In Silico Prediction of HLA-Associated Drug Hypersensitivity." Biophysical Journal 112, no. 3 (February 2017): 293a—294a. http://dx.doi.org/10.1016/j.bpj.2016.11.1590.

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Ludin, Philipp, Ben Woodcroft, Stuart A. Ralph, and Pascal Mäser. "In silico prediction of antimalarial drug target candidates." International Journal for Parasitology: Drugs and Drug Resistance 2 (December 2012): 191–99. http://dx.doi.org/10.1016/j.ijpddr.2012.07.002.

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Islam, Rainul, Sumit Maji, Souparna Kabiraj, Umme Habib, Rohan Pal, Somenath Bhattacharya, Soumallya Chakraborty, and Dr Arin Bhattacharjee. "Role of in silico Drug Design in Pharmaceutical Sciences." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2358–67. http://dx.doi.org/10.22214/ijraset.2022.42836.

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Abstract: In silico drug design is the study to identify, develop, analyze, optimize drugs or biologically cum pharmaceutically active compounds by using computerized software programs as well web servers. In silico drug design is commonly known as computer aided drug design or CADD in short. This technique shows a vital role in preclinical drug design and development. CADD can improve the speed of drug design. It reduces time as well as total cost of the experiments. Potent cum suitable molecules are prepared after performing in silico drug design including CADD. Various applications like confirmation generation, homology modeling, multiple sequence alignment, molecular docking study, generation of Pharmacophores, virtual screening, de novo drug design, QSAR (Quantitative structure activity relationships) study, molecular modeling, in silico ADMET (Absorption, distribution, metabolism, excretion and toxicity) prediction of CADD has been implemented to design newer molecules. The current study focuses on different strategies cum approaches through computer aided drug designing applied to find potent, efficient and safe molecules in the field of drug discovery. Keywords: CADD, drug design, molecular docking, Pharmacophores, virtual screening, de novo drug design, QSAR, molecular modeling.

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Ani R, Anand P S, Sreenath B, and Deepa O S. "In Silico Prediction Tool for Drug-likeness of Compounds based on Ligand Based Screening." International Journal of Research in Pharmaceutical Sciences 11, no. 4 (October 6, 2020): 6273–81. http://dx.doi.org/10.26452/ijrps.v11i4.3310.

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Drug Likeness prediction is a time-consuming and tedious process. An in-vitro method the drug development takes a long time to come to market. The failure rate is also another one to think about in this method. There are many in-silico methods currently available and developing to help the drug discovery and development process. Many online tools are available for predicting and classifying a drug after analyzing the drug-likeness properties of compounds. But most tools have their advantages and disadvantages. In this study, a tool is developed to predict the drug-likeness of compounds given as input to this software. This may help the chemists in analyzing a compound before actually preparing a compound for the drug discovery process. The tool includes both descriptor-based calculation and fingerprint-based calculation of the particular compounds. The descriptor-calculation also includes a set of rules and filters like Lipinski’s rule, Ghose filter, Veber filter and BBB likeness. The previous studies proved that the fingerprint-based prediction is more accurate than descriptor-based prediction. So, in the current study, the drug-likeness prediction tool incorporated the molecular descriptors and fingerprint-based calculations based on five different fingerprint types. The current study incorporated five different machine learning algorithms for prediction of drug-likeness and selected the algorithm, which has a high accuracy rate. When a chemist inputs a particular compound in SMILES format, the drug-likeness prediction tool predicts whether the given candidate compound is drug or non-drug.

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Chi, Cheng-Ting, Ming-Han Lee, Ching-Feng Weng, and Max K. Leong. "In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach." International Journal of Molecular Sciences 20, no. 13 (June 28, 2019): 3170. http://dx.doi.org/10.3390/ijms20133170.

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Oral administration is the preferred and predominant route of choice for medication. As such, drug absorption is one of critical drug metabolism and pharmacokinetics (DM/PK) parameters that should be taken into consideration in the process of drug discovery and development. The cell-free in vitro parallel artificial membrane permeability assay (PAMPA) has been adopted as the primary screening to assess the passive diffusion of compounds in the practical applications. A classical quantitative structure–activity relationship (QSAR) model and a machine learning (ML)-based QSAR model were derived using the partial least square (PLS) scheme and hierarchical support vector regression (HSVR) scheme to elucidate the underlying passive diffusion mechanism and to predict the PAMPA effective permeability, respectively, in this study. It was observed that HSVR executed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical assessments. When applied to the mock test, which was designated to mimic real challenges, HSVR also showed better predictive performance. PLS, conversely, cannot cover some mechanistically interpretable relationships between descriptors and permeability. Accordingly, the synergy of predictive HSVR and interpretable PLS models can be greatly useful in facilitating drug discovery and development by predicting passive diffusion.

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Vilar, Santiago, Eduardo Sobarzo-Sanchez, Lourdes Santana, and Eugenio Uriarte. "Ligand and Structure-based Modeling of Passive Diffusion through the Blood-Brain Barrier." Current Medicinal Chemistry 25, no. 9 (March 29, 2018): 1073–89. http://dx.doi.org/10.2174/0929867324666171106163742.

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Background: Blood-brain barrier transport is an important process to be considered in drug candidates. The blood-brain barrier protects the brain from toxicological agents and, therefore, also establishes a restrictive mechanism for the delivery of drugs into the brain. Although there are different and complex mechanisms implicated in drug transport, in this review we focused on the prediction of passive diffusion through the blood-brain barrier. Methods: We elaborated on ligand-based and structure-based models that have been described to predict the blood-brain barrier permeability. Results: Multiple 2D and 3D QSPR/QSAR models and integrative approaches have been published to establish quantitative and qualitative relationships with the blood-brain barrier permeability. We explained different types of descriptors that correlate with passive diffusion along with data analysis methods. Moreover, we discussed the applicability of other types of molecular structure-based simulations, such as molecular dynamics, and their implications in the prediction of passive diffusion. Challenges and limitations of experimental measurements of permeability and in silico predictive methods were also described. Conclusion: Improvements in the prediction of blood-brain barrier permeability from different types of in silico models are crucial to optimize the process of Central Nervous System drug discovery and development.

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Refsgaard, Hanne H. F., Berith F. Jensen, Inge Thøger Christensen, Nina Hagen, and Per B. Brockhoff. "In silico prediction of cytochrome P450 inhibitors." Drug Development Research 67, no. 5 (2006): 417–29. http://dx.doi.org/10.1002/ddr.20108.

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Cheng, Ailan. "In Silico Prediction of Hepatotoxicity." Current Computer Aided-Drug Design 5, no. 2 (June 1, 2009): 122–27. http://dx.doi.org/10.2174/157340909788451883.

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Triveni, S., C. Naresh Babu, E. Bhargav, and M. Vijaya Jyothi. "in silico Design, ADME Prediction, Molecular Docking, Synthesis of Novel Triazoles, Indazoles & Aminopyridines and in vitro Evaluation of Antitubercular Activity." Asian Journal of Chemistry 32, no. 11 (2020): 2713–21. http://dx.doi.org/10.14233/ajchem.2020.22790.

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To design and synthesize novel triazoles, indazoles and aminopyridines from various (thiophene-2-yl)prop-2-en-1-one derivatives as antitubercular leads by in silico and in vitro methods. in silco Drug design, ADME prediction and molecular docking studies were performed to assess drug likeliness and antitubercular potential of all 30 novel triazoles, indazoles and aminopyridines. in silico Drug design studies revealed that the synthetic routes applied were appropriate according to the calculations of Swiss-ADME that measure synthetic accessibility. Most of the synthesized compounds found to have considerable binding score with enoyl ACP reductase enzyme of Mycobacterium tuberculosis. All the synthesized compounds were evaluated for antitubercular potential against Drug Resistant Mycobacterium tuberculosis H37Rv strain by Luciferase reporter assay method. Most of the synthesized compounds exhibited remarkable antitubercular potential against resistant strain.

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Piñeiro-Yáñez, Elena, María José Jiménez-Santos, Gonzalo Gómez-López, and Fátima Al-Shahrour. "In Silico Drug Prescription for Targeting Cancer Patient Heterogeneity and Prediction of Clinical Outcome." Cancers 11, no. 9 (September 13, 2019): 1361. http://dx.doi.org/10.3390/cancers11091361.

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In silico drug prescription tools for precision cancer medicine can match molecular alterations with tailored candidate treatments. These methodologies require large and well-annotated datasets to systematically evaluate their performance, but this is currently constrained by the lack of complete patient clinicopathological data. Moreover, in silico drug prescription performance could be improved by integrating additional tumour information layers like intra-tumour heterogeneity (ITH) which has been related to drug response and tumour progression. PanDrugs is an in silico drug prescription method which prioritizes anticancer drugs combining both biological and clinical evidence. We have systematically evaluated PanDrugs in the Genomic Data Commons repository (GDC). Our results showed that PanDrugs is able to establish an a priori stratification of cancer patients treated with Epidermal Growth Factor Receptor (EGFR) inhibitors. Patients labelled as responders according to PanDrugs predictions showed a significantly increased overall survival (OS) compared to non-responders. PanDrugs was also able to suggest alternative tailored treatments for non-responder patients. Additionally, PanDrugs usefulness was assessed considering spatial and temporal ITH in cancer patients and showed that ITH can be approached therapeutically proposing drugs or combinations potentially capable of targeting the clonal diversity. In summary, this study is a proof of concept where PanDrugs predictions have been correlated to OS and can be useful to manage ITH in patients while increasing therapeutic options and demonstrating its clinical utility.

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Winiwarter, Susanne, Ernst Ahlberg, Edmund Watson, Ioana Oprisiu, Mickael Mogemark, Tobias Noeske, and Nigel Greene. "In silico ADME in drug design – enhancing the impact." ADMET and DMPK 6, no. 1 (March 25, 2018): 15. http://dx.doi.org/10.5599/admet.6.1.470.

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<p>Each year the pharmaceutical industry makes thousands of compounds, many of which do not meet the desired efficacy or pharmacokinetic properties, describing the absorption, distribution, metabolism and excretion (ADME) behavior. Parameters such as lipophilicity, solubility and metabolic stability can be measured in high throughput in vitro assays. However, a compound needs to be synthesized in order to be tested. In silico models for these endpoints exist, although with varying quality. Such models can be used before synthesis and, together with a potency estimation, influence the decision to make a compound. In practice, it appears that often only one or two predicted properties are considered prior to synthesis, usually including a prediction of lipophilicity. While it is important to use all information when deciding which compound to make, it is somewhat challenging to combine multiple predictions unambiguously. This work investigates the possibility of combining in silico ADME predictions to define the minimum required potency for a specified human dose with sufficient confidence. Using a set of drug discovery compounds,in silico predictions were utilized to compare the relative ranking based on minimum potency calculation with the outcomes from the selection of lead compounds. The approach was also tested on a set of marketed drugs and the influence of the input parameters investigated.</p>

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He, Shuaibing, Tianyuan Ye, Ruiying Wang, Chenyang Zhang, Xuelian Zhang, Guibo Sun, and Xiaobo Sun. "An In Silico Model for Predicting Drug-Induced Hepatotoxicity." International Journal of Molecular Sciences 20, no. 8 (April 17, 2019): 1897. http://dx.doi.org/10.3390/ijms20081897.

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As one of the leading causes of drug failure in clinical trials, drug-induced liver injury (DILI) seriously impeded the development of new drugs. Assessing the DILI risk of drug candidates in advance has been considered as an effective strategy to decrease the rate of attrition in drug discovery. Recently, there have been continuous attempts in the prediction of DILI. However, it indeed remains a huge challenge to predict DILI successfully. There is an urgent need to develop a quantitative structure–activity relationship (QSAR) model for predicting DILI with satisfactory performance. In this work, we reported a high-quality QSAR model for predicting the DILI risk of xenobiotics by incorporating the use of eight effective classifiers and molecular descriptors provided by Marvin. In model development, a large-scale and diverse dataset consisting of 1254 compounds for DILI was built through a comprehensive literature retrieval. The optimal model was attained by an ensemble method, averaging the probabilities from eight classifiers, with accuracy (ACC) of 0.783, sensitivity (SE) of 0.818, specificity (SP) of 0.748, and area under the receiver operating characteristic curve (AUC) of 0.859. For further validation, three external test sets and a large negative dataset were utilized. Consequently, both the internal and external validation indicated that our model outperformed prior studies significantly. Data provided by the current study will also be a valuable source for modeling/data mining in the future.

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Tran, Thi Tuyet Van, Hilal Tayara, and Kil To Chong. "Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction." International Journal of Molecular Sciences 24, no. 3 (January 17, 2023): 1815. http://dx.doi.org/10.3390/ijms24031815.

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Drug distribution is an important process in pharmacokinetics because it has the potential to influence both the amount of medicine reaching the active sites and the effectiveness as well as safety of the drug. The main causes of 90% of drug failures in clinical development are lack of efficacy and uncontrolled toxicity. In recent years, several advances and promising developments in drug distribution property prediction have been achieved, especially in silico, which helped to drastically reduce the time and expense of screening undesired drug candidates. In this study, we provide comprehensive knowledge of drug distribution background, influencing factors, and artificial intelligence-based distribution property prediction models from 2019 to the present. Additionally, we gathered and analyzed public databases and datasets commonly utilized by the scientific community for distribution prediction. The distribution property prediction performance of five large ADMET prediction tools is mentioned as a benchmark for future research. On this basis, we also offer future challenges in drug distribution prediction and research directions. We hope that this review will provide researchers with helpful insight into distribution prediction, thus facilitating the development of innovative approaches for drug discovery.

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Zhang, Tao, Qi Chen, Li Li, Limin Angela Liu, and Dong-Qing Wei. "In Silico Prediction of Cytochrome P450-Mediated Drug Metabolism." Combinatorial Chemistry & High Throughput Screening 14, no. 5 (June 1, 2011): 388–95. http://dx.doi.org/10.2174/138620711795508412.

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Cheng, Feixiong, Weihua Li, Xichuan Wang, Yadi Zhou, Zengrui Wu, Jie Shen, and Yun Tang. "Adverse Drug Events: Database Construction and in Silico Prediction." Journal of Chemical Information and Modeling 53, no. 4 (April 8, 2013): 744–52. http://dx.doi.org/10.1021/ci4000079.

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Katara, Pramod, Atul Grover, Himani Kuntal, and Vinay Sharma. "In silico prediction of drug targets in Vibrio cholerae." Protoplasma 248, no. 4 (December 21, 2010): 799–804. http://dx.doi.org/10.1007/s00709-010-0255-0.

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Kokate, Amit, Xiaoling Li, Paul J. Williams, Parminder Singh, and Bhaskara R. Jasti. "In Silico Prediction of Drug Permeability Across Buccal Mucosa." Pharmaceutical Research 26, no. 5 (January 30, 2009): 1130–39. http://dx.doi.org/10.1007/s11095-009-9831-4.

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Dharani, J., and S. Ravi. "in silico ADMET Screening of Compounds Present in Cyanthillium cinereum (L.) H. Rob." Asian Journal of Chemistry 32, no. 6 (2020): 1421–26. http://dx.doi.org/10.14233/ajchem.2020.22569.

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Drug development involves assessment of absorption, distribution, metabolism, excretion and toxicity (ADMET) increasingly earlier in the discovery process. in silico ADMET studies are expected to reduce the risk of late-stage attrition of drug development and to optimize screening and testing by looking at only the promising compounds. To this end, several in silico approaches for predicting ADMET properties of compounds from their chemical structure have been developed, ranging from data-based approaches. In this study, ADMET prediction has been done for 20 compounds from the plant Cyanthillium cinereum extracts. Some of the compounds were predicted to be non-toxic.

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Lee, Ming-Han, Giang Huong Ta, Ching-Feng Weng, and Max K. Leong. "In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression." International Journal of Molecular Sciences 21, no. 10 (May 19, 2020): 3582. http://dx.doi.org/10.3390/ijms21103582.

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The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure–activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r2 = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q2 = 0.75–0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q2 = 0.78–0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

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Yeh, Shan-Ju, Ruoqiao Chen, Jing Xing, Mengying Sun, Ke Liu, Shreya Paithankar, Jiayu Zhou, and Bin Chen. "Abstract 1927: Transcell: In silico characterization of genomic landscape and cellular responses from gene expressions through a two-step transfer learning." Cancer Research 82, no. 12_Supplement (June 15, 2022): 1927. http://dx.doi.org/10.1158/1538-7445.am2022-1927.

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Abstract Gene expression profiling of new or modified cell lines becomes routine today; however, obtaining comprehensive molecular characterization and cellular responses for a variety of cell lines, including those derived from underrepresented groups, is not trivial when resources are minimal. Using gene expression to predict other measurements has been actively explored; however, systematic investigation of its predictive power in various measurements has not been well studied. We present TransCell, a two-step deep transfer learning framework that utilizes the knowledge derived from pan-cancer tumor samples to predict molecular features and responses. Compared to the five state-of-art methods, TransCell has the best performance in predicting metabolite, gene effect score (or genetic dependency), and drug sensitivity, and has comparable performance in predicting mutation, copy number variation, and protein expression. Notably, TransCell improved the performance by over 50% in drug sensitivity prediction and achieved a correlation of 0.7 in gene effect score prediction. Furthermore, predicted drug sensitivities revealed potential repurposing candidates for new 100 pediatric cancer cell lines, and predicted gene effect scores reflected BRAF resistance in melanoma cell lines. Together, TransCell demonstrates its remarkable predictive power that enables in silico molecular characterization of understudied cell lines. Citation Format: Shan-Ju Yeh, Ruoqiao Chen, Jing Xing, Mengying Sun, Ke Liu, Shreya Paithankar, Jiayu Zhou, Bin Chen. Transcell: In silico characterization of genomic landscape and cellular responses from gene expressions through a two-step transfer learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1927.

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Agamah, Francis E., Gaston K. Mazandu, Radia Hassan, Christian D. Bope, Nicholas E. Thomford, Anita Ghansah, and Emile R. Chimusa. "Computational/in silico methods in drug target and lead prediction." Briefings in Bioinformatics 21, no. 5 (November 10, 2019): 1663–75. http://dx.doi.org/10.1093/bib/bbz103.

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Abstract Drug-like compounds are most of the time denied approval and use owing to the unexpected clinical side effects and cross-reactivity observed during clinical trials. These unexpected outcomes resulting in significant increase in attrition rate centralizes on the selected drug targets. These targets may be disease candidate proteins or genes, biological pathways, disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes of biological network or molecular functions. This is generally linked to several factors, including incomplete knowledge on the drug targets and unpredicted pharmacokinetic expressions upon target interaction or off-target effects. A method used to identify targets, especially for polygenic diseases, is essential and constitutes a major bottleneck in drug development with the fundamental stage being the identification and validation of drug targets of interest for further downstream processes. Thus, various computational methods have been developed to complement experimental approaches in drug discovery. Here, we present an overview of various computational methods and tools applied in predicting or validating drug targets and drug-like molecules. We provide an overview on their advantages and compare these methods to identify effective methods which likely lead to optimal results. We also explore major sources of drug failure considering the challenges and opportunities involved. This review might guide researchers on selecting the most efficient approach or technique during the computational drug discovery process.

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Daoud, Nour El-Huda, Pobitra Borah, Pran Kishore Deb, Katharigatta N. Venugopala, Wafa Hourani, Muhammed Alzweiri, Sanaa K. Bardaweel, and Vinod Tiwari. "ADMET Profiling in Drug Discovery and Development: Perspectives of In Silico, In Vitro and Integrated Approaches." Current Drug Metabolism 22, no. 7 (September 14, 2021): 503–22. http://dx.doi.org/10.2174/1389200222666210705122913.

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In the drug discovery setting, undesirable ADMET properties of a pharmacophore with good predictive power obtained after a tedious drug discovery and development process may lead to late-stage attrition. The earlystage ADMET profiling has brought a new dimension to lead drug development. Although several high-throughput in vitro models are available for ADMET profiling, the in silico methods are gaining more importance because of their economic and faster prediction ability without the requirements of tedious and expensive laboratory resources. Nonetheless, in silico ADMET tools alone are not accurate, and therefore, ideally adopted along with in vitro and or in vivo methods in order to enhance the predictability power. This review summarizes the significance and challenges associated with the application of in silico tools as well as the possible scope of in vitro models for integration to improve the ADMET predictability power of these tools.

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Robles-Loaiza, Alberto A., Edgar A. Pinos-Tamayo, Bruno Mendes, Josselyn A. Ortega-Pila, Carolina Proaño-Bolaños, Fabien Plisson, Cátia Teixeira, Paula Gomes, and José R. Almeida. "Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity." Pharmaceuticals 15, no. 3 (March 8, 2022): 323. http://dx.doi.org/10.3390/ph15030323.

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Peptides have positively impacted the pharmaceutical industry as drugs, biomarkers, or diagnostic tools of high therapeutic value. However, only a handful have progressed to the market. Toxicity is one of the main obstacles to translating peptides into clinics. Hemolysis or hemotoxicity, the principal source of toxicity, is a natural or disease-induced event leading to the death of vital red blood cells. Initial screenings for toxicity have been widely evaluated using erythrocytes as the gold standard. More recently, many online databases filled with peptide sequences and their biological meta-data have paved the way toward hemolysis prediction using user-friendly, fast-access machine learning-driven programs. This review details the growing contributions of in silico approaches developed in the last decade for the large-scale prediction of erythrocyte lysis induced by peptides. After an overview of the pharmaceutical landscape of peptide therapeutics, we highlighted the relevance of early hemolysis studies in drug development. We emphasized the computational models and algorithms used to this end in light of historical and recent findings in this promising field. We benchmarked seven predictors using peptides from different data sets, having 7–35 amino acids in length. According to our predictions, the models have scored an accuracy over 50.42% and a minimal Matthew’s correlation coefficient over 0.11. The maximum values for these statistical parameters achieved 100.0% and 1.00, respectively. Finally, strategies for optimizing peptide selectivity were described, as well as prospects for future investigations. The development of in silico predictive approaches to peptide toxicity has just started, but their important contributions clearly demonstrate their potential for peptide science and computer-aided drug design. Methodology refinement and increasing use will motivate the timely and accurate in silico identification of selective, non-toxic peptide therapeutics.

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Petito, Emilio S., David J. R. Foster, Michael B. Ward, and Matthew J. Sykes. "Molecular Modeling Approaches for the Prediction of Selected Pharmacokinetic Properties." Current Topics in Medicinal Chemistry 18, no. 26 (January 24, 2019): 2230–38. http://dx.doi.org/10.2174/1568026619666181220105726.

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Poor profiles of potential drug candidates, including pharmacokinetic properties, have been acknowledged as a significant hindrance to the development of modern therapeutics. Contemporary drug discovery and development would be incomplete without the aid of molecular modeling (in-silico) techniques, allowing the prediction of pharmacokinetic properties such as clearance, unbound fraction, volume of distribution and bioavailability. As with all models, in-silico approaches are subject to their interpretability, a trait that must be balanced with accuracy when considering the development of new methods. The best models will always require reliable data to inform them, presenting significant challenges, particularly when appropriate in-vitro or in-vivo data may be difficult or time-consuming to obtain. This article seeks to review some of the key in-silico techniques used to predict key pharmacokinetic properties and give commentary on the current and future directions of the field.

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Cipriani, Claudia, Maria Pires Pacheco, Ali Kishk, Maryem Wachich, Daniel Abankwa, Elisabeth Schaffner-Reckinger, and Thomas Sauter. "Bruceine D Identified as a Drug Candidate against Breast Cancer by a Novel Drug Selection Pipeline and Cell Viability Assay." Pharmaceuticals 15, no. 2 (January 31, 2022): 179. http://dx.doi.org/10.3390/ph15020179.

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The multi-target effects of natural products allow us to fight complex diseases like cancer on multiple fronts. Unlike docking techniques, network-based approaches such as genome-scale metabolic modelling can capture multi-target effects. However, the incompleteness of natural product target information reduces the prediction accuracy of in silico gene knockout strategies. Here, we present a drug selection workflow based on context-specific genome-scale metabolic models, built from the expression data of cancer cells treated with natural products, to predict cell viability. The workflow comprises four steps: first, in silico single-drug and drug combination predictions; second, the assessment of the effects of natural products on cancer metabolism via the computation of a dissimilarity score between the treated and control models; third, the identification of natural products with similar effects to the approved drugs; and fourth, the identification of drugs with the predicted effects in pathways of interest, such as the androgen and estrogen pathway. Out of the initial 101 natural products, nine candidates were tested in a 2D cell viability assay. Bruceine D, emodin, and scutellarein showed a dose-dependent inhibition of MCF-7 and Hs 578T cell proliferation with IC50 values between 0.7 to 65 μM, depending on the drug and cell line. Bruceine D, extracted from Brucea javanica seeds, showed the highest potency.

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Chipofya, Mapopa, Hilal Tayara, and Kil To Chong. "Drug Therapeutic-Use Class Prediction and Repurposing Using Graph Convolutional Networks." Pharmaceutics 13, no. 11 (November 10, 2021): 1906. http://dx.doi.org/10.3390/pharmaceutics13111906.

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An important stage in the process of discovering new drugs is when candidate molecules are tested of their efficacy. It is reported that testing drug efficacy empirically costs billions of dollars in the drug discovery pipeline. As a mechanism of expediting this process, researchers have resorted to using computational methods to predict the action of molecules in silico. Here, we present a way of predicting the therapeutic-use class of drugs from chemical structures only using graph convolutional networks. In comparison with existing methods which use fingerprints or images as training samples, our approach has yielded better results in all metrics under consideration. In particular, validation accuracy increased from 83–88% to 86–90% for single label tasks. Similarly, the model achieved an accuracy of over 88% on new test data. Finally, our multi-label classification model made new predictions which indicated that some of the drugs could have other therapeutic uses other than those indicated in the dataset. We performed a literature-based evaluation of these predictions and found evidence that validates them. This renders the model a potential tool to be used in search of drugs that are candidates for repurposing.

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Clark, David E. "In silico prediction of blood–brain barrier permeation." Drug Discovery Today 8, no. 20 (October 2003): 927–33. http://dx.doi.org/10.1016/s1359-6446(03)02827-7.

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Sun, Pingping, Sijia Guo, Jiahang Sun, Liming Tan, Chang Lu, and Zhiqiang Ma. "Advances in In-silico B-cell Epitope Prediction." Current Topics in Medicinal Chemistry 19, no. 2 (March 28, 2019): 105–15. http://dx.doi.org/10.2174/1568026619666181130111827.

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Identification of B-cell epitopes in target antigens is one of the most crucial steps for epitopebased vaccine development, immunodiagnostic tests, antibody production, and disease diagnosis and therapy. Experimental methods for B-cell epitope mapping are time consuming, costly and labor intensive; in the meantime, various in-silico methods are proposed to predict both linear and conformational B-cell epitopes. The accurate identification of B-cell epitopes presents major challenges for immunoinformaticians. In this paper, we have comprehensively reviewed in-silico methods for B-cell epitope identification. The aim of this review is to stimulate the development of better tools which could improve the identification of B-cell epitopes, and further for the development of therapeutic antibodies and diagnostic tools.

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Lin, Jiaying, Min Li, Wenyao Mak, Yufei Shi, Xiao Zhu, Zhijia Tang, Qingfeng He, and Xiaoqiang Xiang. "Applications of In Silico Models to Predict Drug-Induced Liver Injury." Toxics 10, no. 12 (December 14, 2022): 788. http://dx.doi.org/10.3390/toxics10120788.

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Drug-induced liver injury (DILI) is a major cause of the withdrawal of pre-marketed drugs, typically attributed to oxidative stress, mitochondrial damage, disrupted bile acid homeostasis, and innate immune-related inflammation. DILI can be divided into intrinsic and idiosyncratic DILI with cholestatic liver injury as an important manifestation. The diagnosis of DILI remains a challenge today and relies on clinical judgment and knowledge of the insulting agent. Early prediction of hepatotoxicity is an important but still unfulfilled component of drug development. In response, in silico modeling has shown good potential to fill the missing puzzle. Computer algorithms, with machine learning and artificial intelligence as a representative, can be established to initiate a reaction on the given condition to predict DILI. DILIsym is a mechanistic approach that integrates physiologically based pharmacokinetic modeling with the mechanisms of hepatoxicity and has gained increasing popularity for DILI prediction. This article reviews existing in silico approaches utilized to predict DILI risks in clinical medication and provides an overview of the underlying principles and related practical applications.

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Isogai, Hideto, and Noriaki Hirayama. "In Silico Prediction of Interactions between Site II on Human Serum Albumin and Profen Drugs." ISRN Pharmaceutics 2013 (March 6, 2013): 1–8. http://dx.doi.org/10.1155/2013/818364.

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Since binding of a drug molecule to human serum albumin (HSA) significantly affects the pharmacokinetics of the drug, it is highly desirable to predict the binding affinity of the drug. Profen drugs are a widely used class of nonsteroidal anti-inflammatory drugs and it has been reported that several members of the profen class specifically bind to one of the main binding sites named site II. The actual binding mode of only ibuprofen has been directly confirmed by X-ray crystallography. Therefore, it is of interest whether other profen drugs are site II binders. Docking simulations using multiple template structures of HSA from three crystal structures of complexes between drugs and HSA have demonstrated that most of the currently available profen drugs should be site II binders.

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Panyatip, Panyada, Nadtanet Nunthaboot, and Ploenthip Puthongking. "In Silico ADME, Metabolism Prediction and Hydrolysis Study of Melatonin Derivatives." International Journal of Tryptophan Research 13 (January 2020): 117864692097824. http://dx.doi.org/10.1177/1178646920978245.

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Melatonin (MLT) is a well-known pineal hormone possessed with remarkable biological activities. However, its low oral bioavailability and high first-pass metabolism rate are important pharmacokinetics problems. Therefore, 5 MLT derivatives (1-5) were designed and synthesised in our group to solve these problems. In this work, in silico analysis of all synthetic derivatives for pharmacokinetic and drug-likeness parameters were predicted by SwissADME software. The results revealed that all derivatives (1-5) met the requirements for ideal oral bioavailability and CNS drugs. The molecular docking showed that the acetyl-MLT derivative (1) and the un-substitution at N1-position derivative 5 would be substrates of CYP1A2, while the lipophilic substituted N1-position derivatives 2-4 could not be metabolised by CYP1A2. Moreover, all N-amide derivatives (1-4) were hydrolysed and released less than 2.33% MLT after 4-hour incubation in 80% human plasma. It seemed that these derivatives preferred to behave like drugs rather than prodrugs of MLT. These findings confirmed that the addition of bulky groups at the N1-position of the MLT core could prolong the half-life, increase drug absorption and penetrate the blood brain barrier into the CNS.

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Wu, Yu-Wen, Giang Huong Ta, Yi-Chieh Lung, Ching-Feng Weng, and Max K. Leong. "In Silico Prediction of Skin Permeability Using a Two-QSAR Approach." Pharmaceutics 14, no. 5 (April 28, 2022): 961. http://dx.doi.org/10.3390/pharmaceutics14050961.

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Topical and transdermal drug delivery is an effective, safe, and preferred route of drug administration. As such, skin permeability is one of the critical parameters that should be taken into consideration in the process of drug discovery and development. The ex vivo human skin model is considered as the best surrogate to evaluate in vivo skin permeability. This investigation adopted a novel two-QSAR scheme by collectively incorporating machine learning-based hierarchical support vector regression (HSVR) and classical partial least square (PLS) to predict the skin permeability coefficient and to uncover the intrinsic permeation mechanism, respectively, based on ex vivo excised human skin permeability data compiled from the literature. The derived HSVR model functioned better than PLS as represented by the predictive performance in the training set, test set, and outlier set in addition to various statistical estimations. HSVR also delivered consistent performance upon the application of a mock test, which purposely mimicked the real challenges. PLS, contrarily, uncovered the interpretable relevance between selected descriptors and skin permeability. Thus, the synergy between interpretable PLS and predictive HSVR models can be of great use for facilitating drug discovery and development by predicting skin permeability.

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Islam, Sk Mazharul, Sk Md Mosaddek Hossain, and Sumanta Ray. "DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation." PLOS ONE 16, no. 2 (February 19, 2021): e0246920. http://dx.doi.org/10.1371/journal.pone.0246920.

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In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artificial intelligence (AI) approaches have boosted drug repurposing in terms of throughput and accuracy enormously. However, with the growing number of drugs, targets and their massive interactions produce imbalanced data which may not be suitable as input to the classification model directly. Here, we have proposed DTI-SNNFRA, a framework for predicting drug-target interaction (DTI), based on shared nearest neighbour (SNN) and fuzzy-rough approximation (FRA). It uses sampling techniques to collectively reduce the vast search space covering the available drugs, targets and millions of interactions between them. DTI-SNNFRA operates in two stages: first, it uses SNN followed by a partitioning clustering for sampling the search space. Next, it computes the degree of fuzzy-rough approximations and proper degree threshold selection for the negative samples’ undersampling from all possible interaction pairs between drugs and targets obtained in the first stage. Finally, classification is performed using the positive and selected negative samples. We have evaluated the efficacy of DTI-SNNFRA using AUC (Area under ROC Curve), Geometric Mean, and F1 Score. The model performs exceptionally well with a high prediction score of 0.95 for ROC-AUC. The predicted drug-target interactions are validated through an existing drug-target database (Connectivity Map (Cmap)).

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Bruno, Agostino, Gabriele Costantino, Luca Sartori, and Marco Radi. "The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization." Current Medicinal Chemistry 26, no. 21 (September 19, 2019): 3838–73. http://dx.doi.org/10.2174/0929867324666171107101035.

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Background: Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug. Methods: In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan. Results: A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given. Conclusion: The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.

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Güneş, Serdar Sinan, Çağrı Yeşil, Enise Ece Gurdal, Emin Erkan Korkmaz, Mine Yarım, Ahmet Aydın, and Hande Sipahi. "Primum non nocere: In silico prediction of adverse drug reactions of antidepressant drugs." Computational Toxicology 18 (May 2021): 100165. http://dx.doi.org/10.1016/j.comtox.2021.100165.

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Huang, Shuheng, Linxin Chen, Hu Mei, Duo Zhang, Tingting Shi, Zuyin Kuang, Yu Heng, Lei Xu, and Xianchao Pan. "In Silico Prediction of the Dissociation Rate Constants of Small Chemical Ligands by 3D-Grid-Based VolSurf Method." International Journal of Molecular Sciences 21, no. 7 (April 2, 2020): 2456. http://dx.doi.org/10.3390/ijms21072456.

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Accumulated evidence suggests that binding kinetic properties—especially dissociation rate constant or drug-target residence time—are crucial factors affecting drug potency. However, quantitative prediction of kinetic properties has always been a challenging task in drug discovery. In this study, the VolSurf method was successfully applied to quantitatively predict the koff values of the small ligands of heat shock protein 90α (HSP90α), adenosine receptor (AR) and p38 mitogen-activated protein kinase (p38 MAPK). The results showed that few VolSurf descriptors can efficiently capture the key ligand surface properties related to dissociation rate; the resulting models demonstrated to be extremely simple, robust and predictive in comparison with available prediction methods. Therefore, it can be concluded that the VolSurf-based prediction method can be widely applied in the ligand-receptor binding kinetics and de novo drug design researches.

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van de Waterbeemd, Han, and Eric Gifford. "ADMET in silico modelling: towards prediction paradise?" Nature Reviews Drug Discovery 2, no. 3 (March 2003): 192–204. http://dx.doi.org/10.1038/nrd1032.

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Moroy, Gautier, Virginie Y. Martiny, Philippe Vayer, Bruno O. Villoutreix, and Maria A. Miteva. "Toward in silico structure-based ADMET prediction in drug discovery." Drug Discovery Today 17, no. 1-2 (January 2012): 44–55. http://dx.doi.org/10.1016/j.drudis.2011.10.023.

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Cristini, V., H. Frieboes, and J. Fruehauf. "Predictive computer simulations of tumor drug response demonstrate that 3-D hypoxic gradients significantly increase drug resistance." Journal of Clinical Oncology 24, no. 18_suppl (June 20, 2006): 2071. http://dx.doi.org/10.1200/jco.2006.24.18_suppl.2071.

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2071 Background: We created a three-dimensional physiologically based computer (in-silico) model of cancer based on a description of biological events at the cellular scale with input variables determined from patient specific information, such as in-vitro drug response experiments and in-vivo tumor imaging, with the long term goal of individualized treatment selection. The central hypothesis is that such a model that incorporates basic tumor growth kinetics information is capable of representing and predicting tumor response to chemotherapy. Methods: We measured in-vitro tumor growth and drug response for Doxorubicin sensitive and resistant MCF-7 breast cancer cells through trypan blue exclusion counts, tridiated thymidine incorporation, and the XTT assay. We used these results of parameter-based statistics to define input variables to our in-silico model of cancer, and ran computer simulations to measure the drug response predicted by the model. Results: The computer model could accurately predict the in-vitro response of drug sensitive and resistant MCF-7 breast cancer cells. The model also predicted that gradients of oxygen and nutrient in a tumor microenvironment, whether naturally occurring or induced by treatment, and which in previous work we found could increase the invasive capability of tumor cells and destabilize tumor morphology, could also contribute to acquired drug resistance by increasing the population of quiescent cells. Conclusions: We demonstrated that a rigorously, experimentally calibrated computer model of cancer is accurately predictive of in-vitro tumor response to chemotherapeutic drugs, and established that this model offers a means to quantitatively study tumor drug response. We did this through a grounds-up physical representation of tumor biology, not by fitting to experimental data. This validation begins the path to computational modeling and more efficient prediction of in-vivo tumor response to chemotherapy. No significant financial relationships to disclose.

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Long, Yahui, Min Wu, Yong Liu, Chee Keong Kwoh, Jiawei Luo, and Xiaoli Li. "Ensembling graph attention networks for human microbe–drug association prediction." Bioinformatics 36, Supplement_2 (December 2020): i779—i786. http://dx.doi.org/10.1093/bioinformatics/btaa891.

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Abstract Motivation Human microbes get closely involved in an extensive variety of complex human diseases and become new drug targets. In silico methods for identifying potential microbe–drug associations provide an effective complement to conventional experimental methods, which can not only benefit screening candidate compounds for drug development but also facilitate novel knowledge discovery for understanding microbe–drug interaction mechanisms. On the other hand, the recent increased availability of accumulated biomedical data for microbes and drugs provides a great opportunity for a machine learning approach to predict microbe–drug associations. We are thus highly motivated to integrate these data sources to improve prediction accuracy. In addition, it is extremely challenging to predict interactions for new drugs or new microbes, which have no existing microbe–drug associations. Results In this work, we leverage various sources of biomedical information and construct multiple networks (graphs) for microbes and drugs. Then, we develop a novel ensemble framework of graph attention networks with a hierarchical attention mechanism for microbe–drug association prediction from the constructed multiple microbe–drug graphs, denoted as EGATMDA. In particular, for each input graph, we design a graph convolutional network with node-level attention to learn embeddings for nodes (i.e. microbes and drugs). To effectively aggregate node embeddings from multiple input graphs, we implement graph-level attention to learn the importance of different input graphs. Experimental results under different cross-validation settings (e.g. the setting for predicting associations for new drugs) showed that our proposed method outperformed seven state-of-the-art methods. Case studies on predicted microbe–drug associations further demonstrated the effectiveness of our proposed EGATMDA method. Availability Source codes and supplementary materials are available at: https://github.com/longyahui/EGATMDA/ Supplementary information Supplementary data are available at Bioinformatics online.

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Journal articles on the topic 'In silico drug prediction'

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