The ChEMBL database in 2017
ChEMBL is an open large-scale bioactivity database (https://www. ebi. ac. uk/chembl),
previously described in the 2012 and 2014 Nucleic Acids Research Database Issues. Since …
previously described in the 2012 and 2014 Nucleic Acids Research Database Issues. Since …
Accelerated dinuclear palladium catalyst identification through unsupervised machine learning
JA Hueffel, T Sperger, I Funes-Ardoiz, JS Ward… - Science, 2021 - science.org
Although machine learning bears enormous potential to accelerate developments in
homogeneous catalysis, the frequent need for extensive experimental data can be a …
homogeneous catalysis, the frequent need for extensive experimental data can be a …
InstaDock: A single-click graphical user interface for molecular docking-based virtual high-throughput screening
Exploring protein–ligand interactions is a subject of immense interest, as it provides deeper
insights into molecular recognition, mechanism of interaction and subsequent functions …
insights into molecular recognition, mechanism of interaction and subsequent functions …
An open source chemical structure curation pipeline using RDKit
Abstract Background The ChEMBL database is one of a number of public databases that
contain bioactivity data on small molecule compounds curated from diverse sources …
contain bioactivity data on small molecule compounds curated from diverse sources …
Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases
The identification of interactions between drugs/compounds and their targets is crucial for
the development of new drugs. In vitro screening experiments (ie bioassays) are frequently …
the development of new drugs. In vitro screening experiments (ie bioassays) are frequently …
Tinker 8: software tools for molecular design
The Tinker software, currently released as version 8, is a modular molecular mechanics and
dynamics package written primarily in a standard, easily portable dialect of Fortran 95 with …
dynamics package written primarily in a standard, easily portable dialect of Fortran 95 with …
How accurately can one predict drug binding modes using AlphaFold models?
Computational prediction of protein structure has been pursued intensely for decades,
motivated largely by the goal of using structural models for drug discovery. Recently …
motivated largely by the goal of using structural models for drug discovery. Recently …
Extending the applicability of the ANI deep learning molecular potential to sulfur and halogens
C Devereux, JS Smith, KK Huddleston… - Journal of Chemical …, 2020 - ACS Publications
Machine learning (ML) methods have become powerful, predictive tools in a wide range of
applications, such as facial recognition and autonomous vehicles. In the sciences …
applications, such as facial recognition and autonomous vehicles. In the sciences …
A practical guide to machine-learning scoring for structure-based virtual screening
Abstract Structure-based virtual screening (SBVS) via docking has been used to discover
active molecules for a range of therapeutic targets. Chemical and protein data sets that …
active molecules for a range of therapeutic targets. Chemical and protein data sets that …
DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks
Motivation Drug discovery demands rapid quantification of compound–protein interaction
(CPI). However, there is a lack of methods that can predict compound–protein affinity from …
(CPI). However, there is a lack of methods that can predict compound–protein affinity from …