[HTML][HTML] CADD, AI and ML in drug discovery: A comprehensive review
D Vemula, P Jayasurya, V Sushmitha, YN Kumar… - European Journal of …, 2023 - Elsevier
Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest
because of its potential to expedite and lower the cost of the drug development process …
because of its potential to expedite and lower the cost of the drug development process …
[HTML][HTML] Computational methods in drug discovery
SP Leelananda, S Lindert - Beilstein journal of organic …, 2016 - beilstein-journals.org
The process for drug discovery and development is challenging, time consuming and
expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut …
expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut …
[HTML][HTML] GNINA 1.0: molecular docking with deep learning
Molecular docking computationally predicts the conformation of a small molecule when
binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline …
binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline …
[HTML][HTML] A guide to in silico drug design
Y Chang, BA Hawkins, JJ Du, PW Groundwater… - Pharmaceutics, 2023 - mdpi.com
The drug discovery process is a rocky path that is full of challenges, with the result that very
few candidates progress from hit compound to a commercially available product, often due …
few candidates progress from hit compound to a commercially available product, often due …
SwissParam: a fast force field generation tool for small organic molecules
V Zoete, MA Cuendet, A Grosdidier… - Journal of …, 2011 - Wiley Online Library
The drug discovery process has been deeply transformed recently by the use of
computational ligand‐based or structure‐based methods, helping the lead compounds …
computational ligand‐based or structure‐based methods, helping the lead compounds …
Free energy calculations by the molecular mechanics Poisson− Boltzmann surface area method
N Homeyer, H Gohlke - Molecular informatics, 2012 - Wiley Online Library
Detailed knowledge of how molecules recognize interaction partners and of the
conformational preferences of biomacromolecules is pivotal for understanding biochemical …
conformational preferences of biomacromolecules is pivotal for understanding biochemical …
Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening
QU Ain, A Aleksandrova, FD Roessler… - Wiley Interdisciplinary …, 2015 - Wiley Online Library
Docking tools to predict whether and how a small molecule binds to a target can be applied
if a structural model of such target is available. The reliability of docking depends, however …
if a structural model of such target is available. The reliability of docking depends, however …
A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
PJ Ballester, JBO Mitchell - Bioinformatics, 2010 - academic.oup.com
Motivation: Accurately predicting the binding affinities of large sets of diverse protein–ligand
complexes is an extremely challenging task. The scoring functions that attempt such …
complexes is an extremely challenging task. The scoring functions that attempt such …
[HTML][HTML] Advances and challenges in protein-ligand docking
SY Huang, X Zou - International journal of molecular sciences, 2010 - mdpi.com
Molecular docking is a widely-used computational tool for the study of molecular recognition,
which aims to predict the binding mode and binding affinity of a complex formed by two or …
which aims to predict the binding mode and binding affinity of a complex formed by two or …
Role of the active-site solvent in the thermodynamics of factor Xa ligand binding
Understanding the underlying physics of the binding of small-molecule ligands to protein
active sites is a key objective of computational chemistry and biology. It is widely believed …
active sites is a key objective of computational chemistry and biology. It is widely believed …