Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions
New drug production, from target identification to marketing approval, takes over 12 years
and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the …
and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the …
Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling
Highlights•Drug discovery has been advanced to a big data era with a large amount of
public data sources available.•Ten V features (volume, velocity, variety, veracity, validity …
public data sources available.•Ten V features (volume, velocity, variety, veracity, validity …
Structure-based drug design with equivariant diffusion models
Abstract Structure-based drug design (SBDD) aims to design small-molecule ligands that
bind with high affinity and specificity to pre-determined protein targets. Generative SBDD …
bind with high affinity and specificity to pre-determined protein targets. Generative SBDD …
Equivariant 3D-conditional diffusion model for molecular linker design
Fragment-based drug discovery has been an effective paradigm in early-stage drug
development. An open challenge in this area is designing linkers between disconnected …
development. An open challenge in this area is designing linkers between disconnected …
P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure
Background Ligand binding site prediction from protein structure has many applications
related to elucidation of protein function and structure based drug discovery. It often …
related to elucidation of protein function and structure based drug discovery. It often …
[HTML][HTML] Integrating structure-based approaches in generative molecular design
Generative molecular design for drug discovery and development has seen a recent
resurgence promising to improve the efficiency of the design-make-test-analyse cycle; by …
resurgence promising to improve the efficiency of the design-make-test-analyse cycle; by …
Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise
DR Koes, MP Baumgartner… - Journal of chemical …, 2013 - ACS Publications
We describe a general methodology for designing an empirical scoring function and provide
smina, a version of AutoDock Vina specially optimized to support high-throughput scoring …
smina, a version of AutoDock Vina specially optimized to support high-throughput scoring …
PDB-wide collection of binding data: current status of the PDBbind database
Z Liu, Y Li, L Han, J Li, J Liu, Z Zhao, W Nie, Y Liu… - …, 2015 - academic.oup.com
Motivation: Molecular recognition between biological macromolecules and organic small
molecules plays an important role in various life processes. Both structural information and …
molecules plays an important role in various life processes. Both structural information and …
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 …
Virtual screening strategies in drug discovery: a critical review
A Lavecchia, C Di Giovanni - Current medicinal chemistry, 2013 - ingentaconnect.com
Virtual screening (VS) is a powerful technique for identifying hit molecules as starting points
for medicinal chemistry. The number of methods and softwares which use the ligand and …
for medicinal chemistry. The number of methods and softwares which use the ligand and …