AIMC Topic: Databases, Pharmaceutical
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Creating the New from the Old: Combinatorial Libraries Generation with Machine-Learning-Based Compound Structure Optimization.
Journal of chemical information and modeling
Feb 15, 2017
The growing computational abilities of various tools that are applied in the broadly understood field of computer-aided drug design have led to the extreme popularity of virtual screening in the search for new biologically active compounds. Most ofte...
Toward a comprehensive drug ontology: extraction of drug-indication relations from diverse information sources.
Journal of biomedical semantics
Jan 10, 2017
BACKGROUND: Drug ontologies could help pharmaceutical researchers overcome information overload and speed the pace of drug discovery, thus benefiting the industry and patients alike. Drug-disease relations, specifically drug-indication relations, are...
Protease Inhibitors in View of Peptide Substrate Databases.
Journal of chemical information and modeling
Jun 9, 2016
Protease substrate profiling has nowadays almost become a routine task for experimentalists, and the knowledge on protease peptide substrates is easily accessible via the MEROPS database. We present a shape-based virtual screening workflow using vROC...
Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.
Pharmaceutical research
Sep 28, 2015
PURPOSE: Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of m...
Comparing a knowledge-driven approach to a supervised machine learning approach in large-scale extraction of drug-side effect relationships from free-text biomedical literature.
BMC bioinformatics
Mar 18, 2015
BACKGROUND: Systems approaches to studying drug-side-effect (drug-SE) associations are emerging as an active research area for both drug target discovery and drug repositioning. However, a comprehensive drug-SE association knowledge base does not exi...
HemOnc.org: A Collaborative Online Knowledge Platform for Oncology Professionals.
Journal of oncology practice
Mar 3, 2015
PURPOSE: Cancer care involves extensive knowledge about numerous chemotherapy drugs and chemotherapy regimens. This information is constantly evolving, and there has been no freely available, comprehensive, centralized repository of chemotherapy info...
Bayesian models trained with HTS data for predicting β-haematin inhibition and in vitro antimalarial activity.
Bioorganic & medicinal chemistry
Dec 20, 2014
A large quantity of high throughput screening (HTS) data for antimalarial activity has become available in recent years. This includes both phenotypic and target-based activity. Realising the maximum value of these data remains a challenge. In this r...
Active-learning strategies in computer-assisted drug discovery.
Drug discovery today
Dec 9, 2014
High-throughput compound screening is time and resource consuming, and considerable effort is invested into screening compound libraries, profiling, and selecting the most promising candidates for further testing. Active-learning methods assist the s...
RepurposeDrugs: an interactive web-portal and predictive platform for repurposing mono- and combination therapies.
Briefings in bioinformatics
May 23, 2024
RepurposeDrugs (https://repurposedrugs.org/) is a comprehensive web-portal that combines a unique drug indication database with a machine learning (ML) predictor to discover new drug-indication associations for approved as well as investigational mon...
Graph-DTI: A New Model for Drug-target Interaction Prediction Based on Heterogenous Network Graph Embedding.
Current computer-aided drug design
Jan 1, 2024
BACKGROUND: In this study, we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI), which integrates various types of information in the heterogeneous network data, and to explore automatic learning of the topol...
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