Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations
Identifying bioactive conformations of small molecules is an essential process for virtual
screening applications relying on three-dimensional structure such as molecular docking …
screening applications relying on three-dimensional structure such as molecular docking …
Conformer Generation for Structure-Based Drug Design: How Many and How Good?
AT McNutt, F Bisiriyu, S Song, A Vyas… - Journal of Chemical …, 2023 - ACS Publications
Conformer generation, the assignment of realistic 3D coordinates to a small molecule, is
fundamental to structure-based drug design. Conformational ensembles are required for …
fundamental to structure-based drug design. Conformational ensembles are required for …
General purpose structure-based drug discovery neural network score functions with human-interpretable pharmacophore maps
BP Brown, J Mendenhall, AR Geanes… - Journal of chemical …, 2021 - ACS Publications
The BioChemical Library (BCL) is an academic open-source cheminformatics toolkit
comprising ligand-based virtual high-throughput screening (vHTS) tools such as quantitative …
comprising ligand-based virtual high-throughput screening (vHTS) tools such as quantitative …
Infinite Physical Monkey: Do Deep Learning Methods Really Perform Better in Conformation Generation?
Conformation Generation is a fundamental problem in drug discovery and cheminformatics.
And organic molecule conformation generation, particularly in vacuum and protein pocket …
And organic molecule conformation generation, particularly in vacuum and protein pocket …
[HTML][HTML] NeuralDock: Rapid and conformation-agnostic docking of small molecules
Virtual screening is a cost-and time-effective alternative to traditional high-throughput
screening in the drug discovery process. Both virtual screening approaches, structure-based …
screening in the drug discovery process. Both virtual screening approaches, structure-based …
Molecular machine learning with conformer ensembles
S Axelrod, R Gomez-Bombarelli - Machine Learning: Science …, 2023 - iopscience.iop.org
Virtual screening can accelerate drug discovery by identifying promising candidates for
experimental evaluation. Machine learning is a powerful method for screening, as it can …
experimental evaluation. Machine learning is a powerful method for screening, as it can …
Apobind: a dataset of ligand unbound protein conformations for machine learning applications in de novo drug design
Protein-ligand complex structures have been utilised to design benchmark machine learning
methods that perform important tasks related to drug design such as receptor binding site …
methods that perform important tasks related to drug design such as receptor binding site …
ENRI: A tool for selecting structure‐based virtual screening target conformations
Finding pharmaceutically relevant target conformations from an arbitrary set of protein
conformations remains a challenge in structure‐based virtual screening (SBVS). The growth …
conformations remains a challenge in structure‐based virtual screening (SBVS). The growth …
Small-Molecule Conformer Generators: Evaluation of Traditional Methods and AI Models on High-Quality Data Sets
Z Wang, H Zhong, J Zhang, P Pan… - Journal of Chemical …, 2023 - ACS Publications
Small-molecule conformer generation (SMCG) is an extremely important task in both ligand-
and structure-based computer-aided drug design, especially during the hit discovery phase …
and structure-based computer-aided drug design, especially during the hit discovery phase …
AtomNet PoseRanker: Enriching ligand pose quality for dynamic proteins in virtual high-throughput screens
KA Stafford, BM Anderson, J Sorenson… - Journal of chemical …, 2022 - ACS Publications
Structure-based, virtual High-Throughput Screening (vHTS) methods for predicting ligand
activity in drug discovery are important when there are no or relatively few known …
activity in drug discovery are important when there are no or relatively few known …