Relevant bibliographies by topics / In silico drug prediction

Academic literature on the topic 'In silico drug prediction'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "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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Dissertations / Theses on the topic "In silico drug prediction"

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Ahlin, Gustav. "In vitro and in silico prediction of drug-drug interactions with transport proteins." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-107492.

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Carrió, Gaspar Pau 1982. "Development of advanced strategies for the prediction of toxicity endpoints in drug development." Doctoral thesis, Universitat Pompeu Fabra, 2015. http://hdl.handle.net/10803/328418.

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Safety concerns are one of the main causes of drug attrition. In these events, the moment at which the drug toxic effects are discovered changes dramatically the importance of the finding; discarding a valuable candidate at clinical testing stages means wasting years of efforts and huge economicinvestments. Even more dramatic is the discovery of toxic effect at post marketing stages, when the drug could have already produced severe side effects on a number of patients. For these reason there is a pushing need of developing methods able to assess the safety of drug candidates at early stages of development. Among these, in silico methods have many advantages, like not even requiring the availability of the compound, not wasting any quantity of it in case it has been already synthesized, being fast, cheap and make no use of animal testing. Unfortunately, in silico prediction methods of toxicity endpoints do not perform always as expected. The reasons are still under debate, but likely reasons are the complexity of the biological phenomena under study and the large structural diversity of the drug candidates, among others. The aim of this thesis is to improve currently used in silico prediction methods for their application to biological endpoints of interest in drug development, with a special emphasis to toxicological endpoints. Here, we report a novel general methodology called ADAN (Applicability Domain Analysis) for assessing the reliability of drug property predictions obtained by in silico methods. Furthermore, we proposed a unifying strategy for the use of in silico predictive methods in this field, defining rational criteria for the application of a whole spectrum of methods; from structural alerts to global QSAR models, including read across and local models. The usefulness of all the proposed methodologies is tested using a systematic analysis on representative datasets, obtaining good results that confirm their validity.
La manca de seguretat és una de les raons principals per la qual els candidats a fàrmacs són descartats. La fase en què els possibles efectes tòxics són identificats és crítica: descartar un candidat en fase clínica implica la pèrdua d'anys d'esforços i enormes inversions econòmiques. Encara pitjor és identificar efectes tòxics un cop el fàrmac està comercialitzat, quan es poden haver produït greus efectes secundaris en pacients. Per aquestes raons hi ha la necessitat de desenvolupar mètodes capaços d'avaluar la seguretat dels candidats a fàrmacs en les primeres etapes. Entre aquests, els mètodes in silico tenen molts avantatges, com no requerir la disponibilitat del compost, no perdre cap quantitat en cas que ja s'hagi sintetitzat, ser ràpid, econòmic i no fer ús de l'experimentació amb animals. Per desgràcia, els mètodes de predicció in silico aplicats a criteris d'avaluació de toxicitat no produeixen els resultats adequats. Les raons són objecte de debat, però raons probables són la complexitat dels fenòmens biològics en estudi i la gran diversitat estructural els fàrmacs candidats, entre d'altres. L'objectiu d'aquesta tesi és millorar els mètodes de predicció in silico emprats en l’avaluació de criteris d'interès en el desenvolupament de fàrmacs amb especial èmfasi en els de toxicitat. Presentem una nova metodologia general anomenada ADAN (Applicability Domain Analysis) per avaluar la fiabilitat de les prediccions obtingudes amb mètodes in silico. A més, proposem una estratègia unificada de l’ús de mètodes de predicció in silico emprats en aquest camp; com alertes estructurals, read-across, QSAR local i global. La estratègia incorpora criteris racionals per la seva utilització. Els bons resultats obtinguts amb dades representatives confirmen la validesa de les metodologies.
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Vildhede, Anna. "In vitro and in silico Predictions of Hepatic Transporter-Mediated Drug Clearance and Drug-Drug Interactions in vivo." Doctoral thesis, Uppsala universitet, Institutionen för farmaci, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-241376.

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The liver is the major detoxifying organ, clearing the blood from drugs and other xenobiotics. The extent of hepatic clearance (CL) determines drug exposure and hence, the efficacy and toxicity associated with exposure. Drug-drug interactions (DDIs) that alter the hepatic CL may cause more or less severe outcomes, such as adverse drug reactions. Accurate predictions of drug CL and DDI risk from in vitro data are therefore crucial in drug development. Liver CL depends on several factors including the activities of transporters involved in the hepatic uptake and efflux. The work in this thesis aimed at developing new in vitro and in silico methods to predict hepatic transporter-mediated CL and DDIs in vivo. Particular emphasis was placed on interactions involving the hepatic uptake transporters OATP1B1, OATP1B3, and OATP2B1. These transporters regulate the plasma concentration-time profiles of many drugs including statins. Inhibition of OATP-mediated transport by 225 structurally diverse drugs was investigated in vitro. Several novel inhibitors were identified. The data was used to develop in silico models that could predict OATP inhibitors from molecular structure. Models were developed for static and dynamic predictions of in vivo transporter-mediated drug CL and DDIs. These models rely on a combination of in vitro studies of transport function and mass spectrometry-based quantification of protein expression in the in vitro models and liver tissue. By providing estimations of transporter contributions to the overall hepatic uptake/efflux, the method is expected to improve predictions of transporter-mediated DDIs. Furthermore, proteins of importance for hepatic CL were quantified in liver tissue and isolated hepatocytes. The isolation of hepatocytes from liver tissue was found to be associated with oxidative stress and degradation of transporters and other proteins expressed in the plasma membrane. This has implications for the use of primary hepatocytes as an in vitro model of the liver. Nevertheless, by taking the altered transporter abundance into account using the method developed herein, transport function in hepatocyte experiments can be scaled to the in vivo situation. The concept of protein expression-dependent in vitro-in vivo extrapolations was illustrated using atorvastatin and pitavastatin as model drugs.
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Bergström, Christel A. S. "Computational and Experimental Models for the Prediction of Intestinal Drug Solubility and Absorption." Doctoral thesis, Uppsala University, Department of Pharmacy, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3593.

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New effective experimental techniques in medicinal chemistry and pharmacology have resulted in a vast increase in the number of pharmacologically interesting compounds. However, the number of new drugs undergoing clinical trial has not augmented at the same pace, which in part has been attributed to poor absorption of the compounds.

The main objective of this thesis was to investigate whether computer-based models devised from calculated molecular descriptors can be used to predict aqueous drug solubility, an important property influencing the absorption process. For this purpose, both experimental and computational studies were performed. A new small-scale shake flask method for experimental solubility determination of crystalline compounds was devised. This method was used to experimentally determine solubility values used for the computational model development and to investigate the pH-dependent solubility of drugs. In the computer-based studies, rapidly calculated molecular descriptors were used to predict aqueous solubility and the melting point, a solid state characteristic of importance for the solubility. To predict the absorption process, drug permeability across the intestinal epithelium was also modeled.

The results show that high quality solubility data of crystalline compounds can be obtained by the small-scale shake flask method in a microtiter plate format. The experimentally determined pH-dependent solubility profiles deviated largely from the profiles predicted by a traditionally used relationship, highlighting the risk of data extrapolation. The in silico solubility models identified the non-polar surface area and partitioned total surface areas as potential new molecular descriptors for solubility. General solubility models of high accuracy were obtained when combining the surface area descriptors with descriptors for electron distribution, connectivity, flexibility and polarity. The used descriptors proved to be related to the solvation of the molecule rather than to solid state properties. The surface area descriptors were also valid for permeability predictions, and the use of the solubility and permeability models in concert resulted in an excellent theoretical absorption classification. To summarize, the experimental and computational models devised in this thesis are improved absorption screening tools applicable to the lead optimization in the drug discovery process.

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Carlert, Sara. "Investigation and Prediction of Small Intestinal Precipitation of Poorly Soluble Drugs : a Study Involving in silico, in vitro and in vivo Assessment." Doctoral thesis, Uppsala universitet, Institutionen för farmaci, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-178053.

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The main objectives of the present project were to increase the understanding of small intestinal precipitation of poorly soluble pharmaceutical drugs, investigate occurrence of crystalline small intestinal precipitation and effects of precipitation on absorption. The aim was to create and evaluate methods of predicting crystalline small intestinal drug precipitation using in vivo, in vitro and in silico models. In vivo small intestinal precipitation from highly supersaturated solutions of two weakly basic model drugs, AZD0865 and mebendazole, was investigated in humans and canine models. Potential precipitation of AZD0865 was investigated by examining dose dependent increases in human maximum plasma concentration and total exposure, which turned out to be dose linear over the range investigated, indicating no significant in vivo precipitation. The small intestinal precipitation of mebendazole was investigated from drug concentrations and amount of solid drug present in dog jejunum as well as through the bioavailability after direct duodenal administration in dogs. It was concluded that mebendazole small intestinal precipitation was limited, and that intestinal supersaturation was measurable for up to 90 minutes. In vitro precipitation methods utilizing simulated or real fasted gastric and intestinal fluids were developed in order to simulate the in vivo precipitation rate. The methods overpredicted in vivo precipitation when absorption of drug was not simulated. An in vitro-in silico approach was therefore developed, where the in vitro method was used for determining the interfacial tension (γ), necessary for describing crystallization in Classical Nucleation Theory (CNT). CNT was evaluated using a third model drug, bicalutamide, and could successfully describe different parts of the crystallization process of the drug. CNT was then integrated into an in silico absorption model. The in vivo precipitation results of AZD0865 and mebendazole were well predicted by the model, but only by allowing the fundamental constant γ to vary with concentration. Thus, the in vitro-in silico approach could be used for small intestinal precipitation prediction if the in vitro concentration closely matched in vivo small intestinal concentrations.
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Weston, Anne. "In silico prediction of protein function." Thesis, King's College London (University of London), 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412922.

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Sandelin, Albin. "In silico prediction of CIS-regulatory elements /." Stockholm, 2004. http://diss.kib.ki.se/2004/91-7349-879-3/.

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Cereto, Massagué Adrià. "Development of tools for in silico drug discovery." Doctoral thesis, Universitat Rovira i Virgili, 2017. http://hdl.handle.net/10803/454678.

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El cribratge virtual és un mètode quimioinformàtic que consisteix en cribrar molècules bioactives de grans bases de dades de molècules petites. Això permet als investigadors d’estalviar-se el cost de provar experimentalment cents o milers de compostos candidats, reduïnt-ne el nombre fins a quantitats manejables. Per a la validació dels mètodes de cribratge virtual calen biblioteques de molècules cimbell. El programari DecoyFinder fou desenvolupat com a aplicació gràfica de fàcil ús per a la construcció de biblioteques de molècules cimbell, i fou posteriorment ampliat amb les troballes de recerca posterior sobre la construcció i rendiment de biblioteques de molècules cimbell. El Protein Data Bank (PDB) és molt útil perquè proporciona estructures tridimensionals per a complexos proteïna-lligand, i per tant, informació sobre com interactuen. Pels mètodes de cribratge virtual que en depenen, n’és extremadament important la seva fiabilitat. El VHELIBS fou desenvolupat com a eina per a inspeccionar i identificar, fàcilment i intuitiva, les estructures fiables del PDB, basant-se en com de bo n’és l’encaix amb els seus corresponents mapes de densitat electrònica. Mentre que el cribratge virtual prova de trobar noves molècules bioactives per determinades dianes, l’enfoc invers també s’empra: arran d’una molècula, cercar-ne dianes amb activitat biològica no documentada. Aquest cribratge invers és conegut en anglès com a “in silico target fishing”, o pesca de dianes “in silico”, i és especialment útil a l’àmbit de la reutilització de fàrmacs En començar aquesta tesi, no hi havia cap plataforma de “target fishing” de lliure accés, i tot i que durant els anys se n’han desenvolupat algunes, en tots els casos la seva predicció de bioactivitat és qualitativa. Per això es desenvolupà una plataforma pròpia de “target fishing” de lliure accés, amb la implementació d’un nou mètode que proporciona la primera predicció quantitativa de bioactivitat per aquest tipus de plataforma.
El cribado virtual es un método quimioinformático que consiste en la criba de moléculas bioactivas de grandes bases de datos de moléculas pequeñas. Esto permite a los investigadores ahorrarse el coste de probar experimentalmente cientos o miles de compuestos candidatos, reduciéndolos hasta cantidades manejables. Para la validación de los métodos de cribado virtual hacen falta bibliotecas de moléculas señuelo. El software DecoyFinder fue desarrollado como aplicación gráfica de fácil uso para la construcción de bibliotecas de moléculas señuelo, y fue posteriormente ampliado con los hallazgos de investigación posterior sobre la construcción i rendimiento de bibliotecas de moléculas señuelo. El Protein Data Bank (PDB) es muy útil porque proporciona estructuras tridimensionales para complejos proteina-ligando, y por tanto, información sobre como interactúan. Para los métodos de cribado virtual que dependen de ellas, es extremadamente importante su fiabilidad. VHELIBS fue desarrollado como herramienta para inspeccionar e identificar, fácil e intuitivamente, las estructuras fiables del PDB, basándose en como de bueno es su encaje con sus correspondientes mapas de densidad electrónica. Mientras que el cribado virtual intenta encontrar nuevas moléculas bioactivas para determinadas dianas, el enfoque inverso también se utiliza: a partir de una molécula, buscar dianas donde presente actividad biológica no documentada. Este cribado inverso es conocido en inglés como “in silico target fishing”, o pesca de dianas “in silico”, y es especialmente útil en el ámbito de la reutilización de fármacos. Al comenzar esta tesis, no había ninguna plataforma de “target fishing” de libre acceso, y aunque durante los años se han desarrollado algunas, en todos los casos su predicción de bioactividad es cualitativa. Por eso se desarrolló una plataforma propia de “target fishing” de libre acceso, con la implementación de un nuevo método que proporciona la primera predicción cuantitativa de bioactividad para este tipo de plataforma.
Virtual screening is a cheminformatics method that consists of screening large small-molecule databases for bioactive molecules. This enables the researcher to avoid the cost of experimentally testing hundreds or thousands of compounds by reducing the number of candidate molecules to be tested to manageable numbers. For their validation, virtual screening approaches need decoy molecule libraries. DecoyFinder was developed as an easy to use graphical application for decoy library building, and later updated after some research into decoy library building and their performance when used for 2D similarity approaches. The Protein Data Bank (PDB) is very useful because it provides 3D structures for protein-ligand complexes and, therefore, information on how certain ligands bind and interact with their targets. For virtual screening apporaches relying on these structures, it is of the utmost importance that the data available on the PDB for the ligand and its binding site are reliable. VHELIBS was developed as a tool to easily and intuitively inspect and identify reliable PDB structures based on the goodness of fitting between ligands and binding sites and their corresponding electron density map. While virtual screening aims to find new bioactive molecules for certain targets, the opposite approach is also used: starting from a given molecule, to search for a biological target for which it presents previously undocumented bioactivity. This reverse screening is known as in silico or computational target fishing or reverse pharmacognosy, and it is specially useful for drug repurposing or repositioning. When this thesis was started, there were no freely available target fishing platforms, but some have been developed during the years. However, they are qualitative in the nature of their activity prediction, and thus we set out to develop a freely accessible target fishing web service implementing a novel method which provides the first quantitative activity prediction: Anglerfish.
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Acoca, Stephane. "In silico methods in drug discovery and development." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=110376.

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Computational drug design methods have become increasingly invaluable in the drug discovery and development process. Throughout this thesis will be described the development and application of methods that are used at every stage of the drug discovery and development pipeline. In Chapter 2 will take a look at the use computational methods towards the understanding and development of two novel Bcl-2 inhibitors, Obatoclax and ABT-737, being developed for the treatment of Cancer. The study proposes certain mechanisms through which ABT-737 displays selectivity towards certain targets within the Bcl-2 family. Additionally, we propose a binding mode for Obatoclax which is in accordance with experimental data. The following Chapter addresses the use of virtual screening for the identification of novel lead compounds. Trypanosoma brucei RNA Editing Ligase 1 was chosen as the target for the development of treatments against Trypanosoma infections and C35, a potent novel inhibitor of the enzyme, was identified. Furthermore, our research shows that the action of C35 extends to inhibition of several critical enzyme activities required for the RNA editing process as well as compromising the integrity of the multiprotein complex which carries it out. The following Chapter takes a look at the use of mass spectrometry data in order to expedite discovery of bioactive compounds in natural products. We developed an algorithm which analyses MS/MS data in order to derive the Molecular Formula of the compound. The novel algorithm obtained a 95% success rate on a test set of 91 compounds. The last Chapter of the thesis explores the use of molecular dynamics to generate a conformational ensemble of targets for virtual screening. Conformational ensembles were generated for a target test set taken from the Directory for Useful Decoys. The results showed that molecular dynamics-based conformational ensembles provided remarkable improvements on 2 of the targets tested due to the enhanced capacity to properly dock compounds in otherwise restricted structures. The last Chapter of the thesis is a general discussion on the work of the thesis and a proposal on how all can be integrated within the drug discovery and development pipeline.
Les méthodes the modélisation sont devenues un outil inestimable dans le processus de découverte et de développement de nouveaux médicaments. Au cours de cette thèse va être décrit le développement et l'application de méthodes utilisés à chaque stage de la découverte et du développement de produits pharmaceutiques. Le Chapitre 2 est un aperçu sur l'utilisation de méthodes computationnelles vers le développement de deux nouveaux inhibiteurs des protéines Bcl-2, Obatoclax et ABT-737, en développement pour le traitement du Cancer. L'étude propose certains mécanismes d'ABT-737 qui expliquent ca sélectivité envers les membres de la famille Bcl-2. De plus, nous proposons un mécanisme d'attachement pour Obatoclax qui conforme aux données expérimentales. Le Chapitre suivant adresse l'utilisation du dépistage virtuel pour l'identification de nouvelles molécules mère. La Ligase de l'Edition d'ARN du Trypanosoma brucei a été choisie comme cible pour le développement de traitements contre des infections dû au Trypanosome et C35 a été identifié comme nouvel inhibiteur de l'enzyme. En outre, notre recherche démontre que l'action de C35 s'étends a l'inhibition de plusieurs enzymes nécessaires pour le mécanisme d'édition de l'ARN en plus de compromettre l'intégrité du complexe multi-protéinique qui l'effectue. Le Chapitre suivant prends regard a l'utilisation de donnes dérivant de la spectrométrie de masse pour but d'accélérer la découverte de molécules bioactives venant de sources naturelles. Nous avons développé un algorithme qui analyse les données MS/MS pour but de dériver la formule moléculaire du composé. Le nouvel algorithme a obtenu un taux de succès s'élevant à 95% sur un ensemble test de 91 molécules. Le dernier Chapitre de la thèse explore l'utilisation de simulations de dynamique moléculaire pour générer en ensemble conformationel de protéines cible pour son utilisation dans le dépistage virtuel. Les ensembles conformationel ont étés généré pour une série test obtenu d'un répertoire attitré 'Directory for Useful Decoys'. Les résultats démontrent que les ensembles conformationel dérivés de la dynamique moléculaire ont apporté des améliorations remarquables sur deux des cibles testées dû à une capacité accrue de placement approprié des molécules dans un site qui est autrement très restreint. Le dernier Chapitre de cette thèse est une discussion générale sur le travail accomplie et une proposition sur la manière dont tous les éléments sont intégrer dans un protocole de découverte et de développement de produits pharmaceutiques.
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Kundu, Kousik [Verfasser], and Rolf [Akademischer Betreuer] Backofen. "In Silico Prediction of Modular Domain-Peptide Interactions." Freiburg : Universität, 2015. http://d-nb.info/1115861883/34.

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Books on the topic "In silico drug prediction"

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Benfenati, Emilio, ed. In Silico Methods for Predicting Drug Toxicity. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-1960-5.

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Benfenati, Emilio, ed. In Silico Methods for Predicting Drug Toxicity. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3609-0.

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Kortagere, Sandhya, ed. In Silico Models for Drug Discovery. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-342-8.

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Kortagere, Sandhya. In silico models for drug discovery. New York: Humana Press, 2013.

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Dressman, J. B., and C. Reppas. Oral drug absorption: Prediction and assessment. 2nd ed. New York: Informa Healthcare USA, 2010.

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B, Dressman J., and Lennernäs Hans, eds. Oral drug absorption: Prediction and assessment. New York: Marcel Dekker, 2000.

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Gabriele, Cruciani, ed. Molecular interaction fields: Applications in drug discovery and ADME prediction. Weinheim: Wiley-VCH, 2005.

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Hinderling, P. H. Drug distribution in the body: In vitro prediction and physiological interpretation. Stuttgart: Gustav Fischer, 1988.

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Stumpf, Walter E. Drug localization in tissues and cells: Receptor microscopic autoradiography : a basis for tissue and cellular pharmacokinetics, drug targeting, delivery, and prediction. Chapel Hill, NC: IDDC-Press, 2003.

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M, Greenwood B., and De Cock Kevin, eds. New and resurgent infections: Prediction, detection, and management of tomorrow's epidemics. Chichester: J. Wiley & Sons, 1998.

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Book chapters on the topic "In silico drug prediction"

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Ali, Mohammed A., Rachel Hemingway, and Martin A. Ott. "In Silico Drug Degradation Prediction." In Methods in Pharmacology and Toxicology, 53–73. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7686-7_3.

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Cronin, Mark T. D. "Chapter 2. In Silico Tools for Toxicity Prediction." In Drug Discovery, 9–25. Cambridge: Royal Society of Chemistry, 2011. http://dx.doi.org/10.1039/9781849733045-00009.

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Yu, Lawrence X., Christopher D. Ellison, and Ajaz S. Hussain. "Predicting Human Oral Bioavailability Using in Silico Models." In Applications of Pharmacokinetic Principles in Drug Development, 53–74. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9216-1_3.

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Ponting, David J., Michael J. Burns, Robert S. Foster, Rachel Hemingway, Grace Kocks, Donna S. MacMillan, Andrew L. Shannon-Little, Rachael E. Tennant, Jessica R. Tidmarsh, and David J. Yeo. "Use of Lhasa Limited Products for the In Silico Prediction of Drug Toxicity." In Methods in Molecular Biology, 435–78. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-1960-5_17.

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Bachmann, Kenneth, and Sean Ekins. "The Potential of In Silico and In Vitro Approaches to Predict In Vivo Drug-Drug Interactions and ADMET/TOX Properties." In Predictive Approaches in Drug Discovery and Development, 307–29. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118230275.ch13.

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Cutinho, Pretisha Flora, C. H. S. Venkataramana, and B. V. Suma. "In Silico Hit Identification, Drug Repurposing, Pharmacokinetic and Toxicity Prediction of c-Met Kinase Inhibitors for Cancer Therapy." In Special Publications, 54–59. Cambridge: Royal Society of Chemistry, 2019. http://dx.doi.org/10.1039/9781839160783-00054.

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Gupta, Praveen Kumar, Mohammed Haseeb Nawaz, Shyam Shankar Mishra, Kruthika Parappa, Akhil Silla, and Raju Hanumegowda. "New Age Approaches to Predictive Healthcare Using In Silico Drug Design and Internet of Things (IoT)." In Sustainable and Energy Efficient Computing Paradigms for Society, 127–51. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51070-1_8.

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Singstad, Bjørn Jostein, Bendik Steinsvåg Dalen, Sandhya Sihra, Nickolas Forsch, and Samuel Wall. "Identifying Ionic Channel Block in a Virtual Cardiomyocyte Population Using Machine Learning Classifiers." In Computational Physiology, 91–109. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05164-7_8.

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AbstractImmature cardiomyocytes, such as those obtained by stem cell differentiation, have been shown to be useful alternatives to mature cardiomyocytes, which are limited in availability and difficult to obtain, for evaluating the behaviour of drugs for treating arrhythmia. In silico models of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) can be used to simulate the behaviour of the transmembrane potential and cytosolic calcium under drug-treated conditions. Simulating the change in action potentials due to various ionic current blocks enables the approximation of drug behaviour. We used eight machine learning classification models to predict partial block of seven possible ion currents $$ (\textit{I}_{\textit{CaL}},\textit{I}_{\textit{Kr}},\textit{I}_{\textit{to}},\textit{I}_{\textit{K1}},\textit{I}_{\textit{Na}},\textit{I}_{\textit{NaL}} and \textit{I}_{\textit{Ks}}) $$ in a simulated dataset containing nearly 4600 action potentials represented as a paired measure of transmembrane potential and cytosolic calcium. Each action potential was generated under 1 $$ \textit{H}_{\textit{z}} $$ pacing. The Convolutional Neural Network outperformed the other models with an average accuracy of predicting partial ionic current block of 93% in noise-free data and 72% accuracy with 3% added random noise. Our results show that $$ \textit{I}_{\textit{CaL}} $$ and $$ \textit{I}_{\textit{Kr}} $$ current block were classified with high accuracy with and without noise. The classification of $$ \textit{I}_{\textit{to}} $$ , $$ \textit{I}_{\textit{K1}} $$ and $$ \textit{I}_{\textit{Na}} $$ current block showed high accuracy at 0% noise, but showed a significant decrease in accuracy when noise was added. Finally, the accuracy of $$ \textit{I}_{\textit{NaL}} $$ and $$ \textit{I}_{\textit{Ks}} $$ classification were relatively lower than the other current blocks at 0% noise and also showed a significant drop in accuracy when noise was added. In conclusion, these machine learning methods may present a pathway for estimating drug response in adult phenotype cardiac systems, but the data must be sufficiently filtered to remove noise before being used with classifier algorithms.
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Amberg, Alexander. "In Silico Methods." In Drug Discovery and Evaluation, 801–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-29804-5_43.

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Matter, Hans, and Wolfgang Schmider. "In-Silico ADME Modeling." In Drug Discovery and Evaluation, 409–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-29804-5_20.

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Conference papers on the topic "In silico drug prediction"

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Llopis-Lorente, Jordi, Beatriz Trenor, and Javier Saiz. "Prediction of Drug-Induced Arrhythmogenic Risk Using In Silico Populations of Models." In 2021 Computing in Cardiology (CinC). IEEE, 2021. http://dx.doi.org/10.23919/cinc53138.2021.9662679.

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El-Khouly, Omar A., Dina I. A. Othman, Amany S. Mostafa, and Mohammed A. M. Massoud. "Thiazolopyrimidine as a Promising Anticancer Pharmacophore: In Silico Drug Design, Molecular Docking and ADMET Prediction Studies." In ECMC 2022. Basel Switzerland: MDPI, 2022. http://dx.doi.org/10.3390/ecmc2022-13313.

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Deb, Subrata, and Anthony Reeves. "<em>In Silico</em> prediction of biopharmaceutical features of remdesivir: A serendipitous drug for COVID-19." In 6th International Electronic Conference on Medicinal Chemistry. Basel, Switzerland: MDPI, 2020. http://dx.doi.org/10.3390/ecmc2020-07301.

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Azmi, Muhammad Bilal. "In Silico Basis to Understand the Molecular Interaction of Human NNATGene With Therapeutic Compounds of Anorexia Nervosa." In INTERNATIONAL CONFERENCE ON BIOLOGICAL RESEARCH AND APPLIED SCIENCE. Jinnah University for Women, Karachi,Pakistan, 2022. http://dx.doi.org/10.37962/ibras/2022/1-2.

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Introduction: Anorexia nervosa (AN)– a perplexing heritable, psychiatric eating disorder condition characterized by low body weight. The prevalence of AN is found to be high in younger age adults with a raised mortality rate. Genetic studies have been insufficient in identifying the role of specific genes that predispose an individual to AN. Objectives: The objective was to explore the role of NNAT (neuronatin) gene variants and its structure based molecular interactions with therapeutic compounds of AN. To investigate the role of structural missense pathogenic variants (SNPs: single nucleotide polymorphism) or change in the expression of NNAT with possibility of AN. Methodology: NNAT gene protein coding sequence, SNPs were extracted and validated from public databases. Gene to gene interactions, protein localization and human tissue-specific expression analysis of NNAT gene showed highest tissue-specific expression in the brain. Estimates of functional impact of SNPs using transcript sequence and machine learning based approaches (in silico algorithms) were computed to investigate the pathogenicity and protein stability of NNAT variants. Sequence alignment, ab initio 3D structure-modeling of wild-type, validation and recognition of binding cavities of NNAT through in silico web based tools were performed. Alternate model prediction for NNAT variants through residue specific mutation approach and structural validation were also done through Chimera tool. The 3D compounds involved in the management of AN were extracted from the Drug Bank database, afterwards energy minimization and rule of drug-likeness were performed. The eligible 3D compounds were docked with identified variants, to evaluate the drugs binding molecular mechanics. Results & Conclusion: Overall, 10 NNAT missense variants were extracted on the basis of minor allele frequency (MAF < 0.001) and other consequence types. Further three variants were selected among ten according to the transcript sequence, which includesrs542858994 (F26L), rs539681368 (C30Y) and rs542858994 (F53L). Structures for these variants’ protein were generated, validated and docked with AN drugs. The functional impact analyses of selected missense SNPs of NNAT showed high risk pathogenicity and can cause changes in the physical and chemical properties of amino acids, thus affecting the function of protein. The computation of binding energies of variants of NNAT with AN compounds strengthen the hypothesis that these variants strongly interact with the AN drugs, hence reducing the level of free NNAT as well as target drugs, for neuronal functioning. Therefore, constitutionally reduced level of NNAT and binding of NNAT variants with AN drugs may serve as the basis to increases the susceptibility towards AN.
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Huang, Hung-Jin, Fuu-Jen Tsai, Jing-Gung Chung, Chang-Hai Tsai, Yuan-Man Hsu, Tin-Yun Ho, Yea-Huey Chang, Da-Tian Bau, Ming-Hsui Tsai, and Calvin Yu-Chian Chen. "Drug Design for XRCC4 in Silico." In 2009 2nd International Conference on Biomedical Engineering and Informatics. IEEE, 2009. http://dx.doi.org/10.1109/bmei.2009.5304961.

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Kumar, Dhananjay, Anshul Sarvate, Sakshi Singh, and Puja Priya. "Comparative modelling and in-silico drug designing." In 2013 IEEE Conference on Information & Communication Technologies (ICT). IEEE, 2013. http://dx.doi.org/10.1109/cict.2013.6558165.

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Hayati, Hamideh, and Yu Feng. "A Precise Scale-Up Method to Predict Particle Delivered Dose in a Human Respiratory System Using Rat Deposition Data: An In Silico Study." In 2020 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dmd2020-9060.

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Abstract As surrogates to human beings, rats are used occasionally to study the therapeutic impact of inhaled pulmonary drug particles in microscale. To speculate human responses from rat studies, scale-up factors are widely used to extrapolate particle lung deposition from rat to human. However, available scale-up methods are highly simplified and not accurate, because they directly use the human-to-rat ratios of body weights (RBW) or lung surface areas (RSA) as the scale-up factor. To find a precise scale-up strategy, an experimentally validated Computational Fluid-Particle Dynamics (CFPD) was employed to simulate the transport and deposition of microparticles in both human and rate respiratory systems, which encompasses the pulmonary routes from mouth/nose to airways up to Generation 17 (G17) for human and G23 for the rat. Microparticles with the same range of Stk/Fr were injected into both models with the airflow at resting conditions. Numerical results indicate that particles (with the size ranging from 1 to 13 μm for humans and 0.6 to 6 μm for rat) have similar deposition pattern (DP) and deposition fraction (DF) in both models, which are resulted from both inertial impaction and gravitational sedimentation effects. A novel correlation is proposed to predict DFs in both human and rat respiratory systems as a function of the ratio of Stokes number to Froude number (Stk/Fr). Using the correlation as the novel scale-up tool, inter-species extrapolations can be precisely done on predicting particle depositions in human respiratory systems based on the deposition data in rats obtained from animal studies.
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Baba, Waqas, and Sajid Maqsood. "Novel antihypertensive and anticholesterolemic peptides from peptic hydrolysates of camel whey proteins." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/qecs2081.

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Hypercholesterolemia and hypertension are major growing concerns that are managed by drugs that inhibit various metabolic enzymes. Milk hydrolysates have been reported to contain various bioactive peptides (BAP) that can inhibit various metabolic enzymes for enhancing human health. As such camel whey proteins were subjected to peptic hydrolysis using a full factorial model (33) with hydrolysis time, temperature, and enzyme concentration as factors. The resulting hydrolysates were analyzed for anti-hypercholesterolemic and hypertensive properties by studying the in vitro inhibition of various enzymatic markers. The hydrolysates with lowest IC50 values were further subjected to LC-MS-QTOF that revealed presence of 185 peptides. Selected peptides that had Peptide Ranker Score greater than 0.8 were further studied for prediction of possible interactions with enzyme markers: pancreatic lipase (PL) cholesterol esterase (CE) and angiotensin converting enzyme (ACE) using in silico analysis. The data generated suggested that most of the peptides could bind active site of PL while as only three peptides could bind active site of CE. Based on higher number of reactive residues in the bioactive peptides (BAP) and greater number of substrate binding sites, FCCLGPVPP was identified as potential CE inhibitory peptide while PAGNFLPPVAAAPVM, MLPLMLPFTMGY, and LRFPL were identified as PL inhibitors. While peptides PAGNFLP, FCCLGPVPP, PAGNFLMNGLMHR, PAVACCLPPLPCHM were identified as potential ACE inhibitors. Molecular docking of selected peptides showed hydrophilic and hydrophobic interactions between peptides and target enzymes. Moreover, due to the importance of renin in managing hypertension, peptides from hydrolysates with high ACE inhibiting potential were predicted for potential to interact with renin using in silico analysis. Molecular docking was subsequently employed to identify how the identified peptides, PVAAAPVM and LRPFL, could interact with renin and potentially inhibit it. Thus, non-bovine (camel) whey hydrolysates might be used as functional ingredients for production of functional foods with antihypertensive and anticholesterolemic properties.
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Chen, Chien-Yu, Da-Tian Bau, Ming-Hsui Tsai, Yuan-Man Hsu, Tin-Yun Ho, Hung-Jin Huang, Yea-Huey Chang, Fuu-Jen Tsai, Chang-Hai Tsai, and Calvin Yu-Chian Chen. "Drug Design for AMP-Activated Protein Kinase Agonists in Silico." In 2009 2nd International Conference on Biomedical Engineering and Informatics. IEEE, 2009. http://dx.doi.org/10.1109/bmei.2009.5304901.

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Pezoulas, Vasileios, Nikos Tachos, and Dimitrios Fotiadis. "Generation of Virtual Patients for in Silico Cardiomyopathies Drug Development." In 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2019. http://dx.doi.org/10.1109/bibe.2019.00126.

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Reports on the topic "In silico drug prediction"

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Idakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, and Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41302.

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Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR activity classes (i.e., agonist, antagonist, inactive, and inconclusive) at the same distribution rates amongst the training, validation and test subsets. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p < 0.001, ANOVA) by 22–27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Further in-depth analyses of chemical scaffolding shed insights on structural alerts for AR agonists/antagonists and inactive/inconclusive compounds, which may aid in future drug discovery and improvement of toxicity prediction modeling.
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Nilmeier, J., J. Fattebert, M. Jacobson, and C. Kalyanaraman. Quantum mechanical approaches to in silico enzyme characterization and drug design. Office of Scientific and Technical Information (OSTI), January 2012. http://dx.doi.org/10.2172/1034511.

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Hsieh, John C. F., Robert L. Jernigan, and Susan J. Lamont. Host-Pathogen Protein-Protein Interaction Prediction Using an in silico Model. Ames (Iowa): Iowa State University, January 2016. http://dx.doi.org/10.31274/ans_air-180814-224.

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Washburn, Ammon Joseph, Thomas James Sherman, Marian Anghel, Cristina Garcia Cardona, and Jason David Gans. Prediction of Drug Response in Cancerous Cell Lines Using Machine Learning Algorithms. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1396149.

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Marusich, Julie, Timothy Lefever, Scott Novak, Bruce Blough, and Jenny Wiley. Prediction and Prevention of Prescription Drug Abuse: Role of Preclinical Assessment of Substance Abuse Liability. Research Triangle Park, NC: RTI Press, July 2013. http://dx.doi.org/10.3768/rtipress.2013.op.0014.1307.

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Liu, Xiaopei, Dan Liu, and Cong’e Tan. Gut microbiome-based machine learning for diagnostic prediction of liver fibrosis and cirrhosis: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2022. http://dx.doi.org/10.37766/inplasy2022.5.0133.

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Review question / Objective: The invasive liver biopsy is the gold standard for the diagnosis of liver cirrhosis. Other non-invasive diagnostic approaches, have been used as alternatives to liver biopsy, however, these methods cannot identify the pathological grade of the lesion. Recently, studies have shown that gut microbiome-based machine learning can be used as a non-invasive diagnostic approach for liver cirrhosis or fibrosis, while it lacks evidence-based support. Therefore, we performed this systematic review and meta-analysis to evaluate its predictive diagnostic value in liver cirrhosis or fibrosis. Condition being studied: Liver fibrosis and cirrhosis. Liver fibrosis refers to excessive deposition of liver fibrous tissue caused by various pathogenic factors, such as hepatitis virus, alcohol, and drug-induced chemical injury. Continuous progression of liver fibrosis can lead to liver cirrhosis.
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