Machine-learning methods for ligand–protein molecular docking
K Crampon, A Giorkallos, M Deldossi, S Baud… - Drug discovery today, 2022 - Elsevier
Artificial intelligence (AI) is often presented as a new Industrial Revolution. Many domains
use AI, including molecular simulation for drug discovery. In this review, we provide an …
use AI, including molecular simulation for drug discovery. In this review, we provide an …
Forging the basis for develo** protein–ligand interaction scoring functions
Z Liu, M Su, L Han, J Liu, Q Yang, Y Li… - Accounts of chemical …, 2017 - ACS Publications
Conspectus In structure-based drug design, scoring functions are widely used for fast
evaluation of protein–ligand interactions. They are often applied in combination with …
evaluation of protein–ligand interactions. They are often applied in combination with …
PLIP 2021: Expanding the scope of the protein–ligand interaction profiler to DNA and RNA
MF Adasme, KL Linnemann, SN Bolz… - Nucleic acids …, 2021 - academic.oup.com
With the growth of protein structure data, the analysis of molecular interactions between
ligands and their target molecules is gaining importance. PLIP, the protein–ligand …
ligands and their target molecules is gaining importance. PLIP, the protein–ligand …
PLIP: fully automated protein–ligand interaction profiler
The characterization of interactions in protein–ligand complexes is essential for research in
structural bioinformatics, drug discovery and biology. However, comprehensive tools are not …
structural bioinformatics, drug discovery and biology. However, comprehensive tools are not …
From machine learning to deep learning: Advances in scoring functions for protein–ligand docking
Molecule docking has been regarded as a routine tool for drug discovery, but its accuracy
highly depends on the reliability of scoring functions (SFs). With the rapid development of …
highly depends on the reliability of scoring functions (SFs). With the rapid development of …
Artificial intelligence in virtual screening: Models versus experiments
A typical drug discovery project involves identifying active compounds with significant
binding potential for selected disease-specific targets. Experimental high-throughput …
binding potential for selected disease-specific targets. Experimental high-throughput …
Scoring functions for protein-ligand binding affinity prediction using structure-based deep learning: a review
The rapid and accurate in silico prediction of protein-ligand binding free energies or binding
affinities has the potential to transform drug discovery. In recent years, there has been a …
affinities has the potential to transform drug discovery. In recent years, there has been a …
Machine learning classification can reduce false positives in structure-based virtual screening
YO Adeshina, EJ Deeds… - Proceedings of the …, 2020 - National Acad Sciences
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 …
positioned to assume a more prominent role in early drug discovery's search for active …
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 …
Machine‐learning scoring functions for structure‐based virtual screening
Molecular docking predicts whether and how small molecules bind to a macromolecular
target using a suitable 3D structure. Scoring functions for structure‐based virtual screening …
target using a suitable 3D structure. Scoring functions for structure‐based virtual screening …