Journal of chemical information and modeling
Sep 9, 2020
Artificial intelligence and multiobjective optimization represent promising solutions to bridge chemical and biological landscapes by addressing the automated design of compounds as a result of a humanlike creative process. In the present study, we ...
Proceedings of the National Academy of Sciences of the United States of America
Jul 15, 2020
With the recent explosion in the size of libraries available for screening, virtual screening is positioned to assume a more prominent role in early drug discovery's search for active chemical matter. In typical virtual screens, however, only about 1...
DNA-encoded small molecule libraries (DELs) have enabled discovery of novel inhibitors for many distinct protein targets of therapeutic value. We demonstrate a new approach applying machine learning to DEL selection data by identifying active molecul...
Journal of computer-aided molecular design
Apr 16, 2020
In drug development, late stage toxicity issues of a compound are the main cause of failure in clinical trials. In silico methods are therefore of high importance to guide the early design process to reduce time, costs and animal testing. Technical a...
The receptor for advanced glycation end products (RAGE) has been identified as a therapeutic target in a host of pathological diseases, including Alzheimer's disease. RAGE is a target with no crystallographic data on inhibitors in complex with RAGE, ...
Liquid chromatography coupled to high-resolution mass spectrometry (HRMS) enables data independent acquisition (DIA) and untargeted screening. However, to avoid the handling of the resulting large dataset, most laboratories in that field still use ta...
Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properti...
Machine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retent...
Journal of computer-aided molecular design
Nov 19, 2019
Development of novel in silico methods for questing novel PgP inhibitors is crucial for the reversal of multi-drug resistance in cancer therapy. Here, we report machine learning based binary classification schemes to identify the PgP inhibitors from ...
Journal of computer-aided molecular design
Nov 16, 2019
We present the performances of our mathematical deep learning (MathDL) models for D3R Grand Challenge 4 (GC4). This challenge involves pose prediction, affinity ranking, and free energy estimation for beta secretase 1 (BACE) as well as affinity ranki...