AIMC Topic: Drug Evaluation, Preclinical
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Machine Learning Models Based on Molecular Fingerprints and an Extreme Gradient Boosting Method Lead to the Discovery of JAK2 Inhibitors.
Journal of chemical information and modeling
Dec 4, 2019
Developing Janus kinase 2 (JAK2) inhibitors has become a significant focus for small-molecule drug discovery programs in recent years because the inhibition of JAK2 may be an effective approach for the treatment of myeloproliferative neoplasm. Here, ...
Development of New Methods Needs Proper Evaluation-Benchmarking Sets for Machine Learning Experiments for Class A GPCRs.
Journal of chemical information and modeling
Nov 22, 2019
New computational approaches for virtual screening applications are constantly being developed. However, before a particular tool is used to search for new active compounds, its effectiveness in the type of task must be examined. In this study, we co...
Machine learning for target discovery in drug development.
Current opinion in chemical biology
Nov 14, 2019
The discovery of macromolecular targets for bioactive agents is currently a bottleneck for the informed design of chemical probes and drug leads. Typically, activity profiling against genetically manipulated cell lines or chemical proteomics is pursu...
Deep learning enables rapid identification of potent DDR1 kinase inhibitors.
Nature biotechnology
Sep 2, 2019
We have developed a deep generative model, generative tensorial reinforcement learning (GENTRL), for de novo small-molecule design. GENTRL optimizes synthetic feasibility, novelty, and biological activity. We used GENTRL to discover potent inhibitors...
Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening.
PloS one
Aug 20, 2019
Recently much effort has been invested in using convolutional neural network (CNN) models trained on 3D structural images of protein-ligand complexes to distinguish binding from non-binding ligands for virtual screening. However, the dearth of reliab...
Artificial Intelligence for Drug Toxicity and Safety.
Trends in pharmacological sciences
Aug 2, 2019
Interventional pharmacology is one of medicine's most potent weapons against disease. These drugs, however, can result in damaging side effects and must be closely monitored. Pharmacovigilance is the field of science that monitors, detects, and preve...
The Missing Pieces of Artificial Intelligence in Medicine.
Trends in pharmacological sciences
Jul 2, 2019
Stakeholders across the entire healthcare chain are looking to incorporate artificial intelligence (AI) into their decision-making process. From early-stage drug discovery to clinical decision support systems, we have seen examples of how AI can impr...
Automating a Magnetic 3D Spheroid Model Technology for High-Throughput Screening.
SLAS technology
Jun 21, 2019
Affordable and physiologically relevant three-dimensional (3D) cell-based assays used in high-throughput screening (HTS) are on the rise in early drug discovery. These technologies have been aided by the recent adaptation of novel microplate treatmen...
Design of Natural-Product-Inspired Multitarget Ligands by Machine Learning.
ChemMedChem
May 16, 2019
A virtual screening protocol based on machine learning models was used to identify mimetics of the natural product (-)-galantamine. This fully automated approach identified eight compounds with bioactivities on at least one of the macromolecular targ...
Identification of the lipid-lowering component of triterpenes from Alismatis rhizoma based on the MRM-based characteristic chemical profiles and support vector machine model.
Analytical and bioanalytical chemistry
May 14, 2019
It has been demonstrated that triterpenes in Alismatis rhizoma (Zexie in Chinese, ZX) contributed to the lipid-lowering effect on high-fat diet-induced hyperlipidemia. Alisol B 23-acetate, one of the abundant triterpenes in ZX, was used as the marker...
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