Improved protein–ligand binding affinity prediction with structure-based deep fusion inference
Predicting accurate protein–ligand binding affinities is an important task in drug discovery
but remains a challenge even with computationally expensive biophysics-based energy …
but remains a challenge even with computationally expensive biophysics-based energy …
fastDRH: a webserver to predict and analyze protein–ligand complexes based on molecular docking and MM/PB (GB) SA computation
Z Wang, H Pan, H Sun, Y Kang, H Liu… - Briefings in …, 2022 - academic.oup.com
Predicting the native or near-native binding pose of a small molecule within a protein
binding pocket is an extremely important task in structure-based drug design, especially in …
binding pocket is an extremely important task in structure-based drug design, especially in …
Forging the basis for developing 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 …
Hac-net: A hybrid attention-based convolutional neural network for highly accurate protein–ligand binding affinity prediction
GW Kyro, RI Brent, VS Batista - Journal of Chemical Information …, 2023 - ACS Publications
Applying deep learning concepts from image detection and graph theory has greatly
advanced protein–ligand binding affinity prediction, a challenge with enormous ramifications …
advanced protein–ligand binding affinity prediction, a challenge with enormous ramifications …
DrugScoreCSDKnowledge-Based Scoring Function Derived from Small Molecule Crystal Data with Superior Recognition Rate of Near-Native Ligand Poses and …
Following the formalism used for the development of the knowledge-based scoring function
DrugScore, new distance-dependent pair potentials are obtained from nonbonded …
DrugScore, new distance-dependent pair potentials are obtained from nonbonded …
[HTML][HTML] High-throughput docking using quantum mechanical scoring
CN Cavasotto, MG Aucar - Frontiers in chemistry, 2020 - frontiersin.org
Today high-throughput docking is one of the most commonly used computational tools in
drug lead discovery. While there has been an impressive methodological improvement in …
drug lead discovery. While there has been an impressive methodological improvement in …
Compound–protein interaction prediction by deep learning: databases, descriptors and models
The screening of compound–protein interactions (CPIs) is one of the most crucial steps in
finding hit and lead compounds. Deep learning (DL) methods for CPI prediction can address …
finding hit and lead compounds. Deep learning (DL) methods for CPI prediction can address …
[HTML][HTML] SeamDock: an interactive and collaborative online docking resource to assist small compound molecular docking
In silico assessment of protein receptor interactions with small ligands is now part of the
standard pipeline for drug discovery, and numerous tools and protocols have been …
standard pipeline for drug discovery, and numerous tools and protocols have been …
Deep docking: a deep learning platform for augmentation of structure based drug discovery
Drug discovery is a rigorous process that requires billion dollars of investments and decades
of research to bring a molecule “from bench to a bedside”. While virtual docking can …
of research to bring a molecule “from bench to a bedside”. While virtual docking can …
Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design
PG Francoeur, T Masuda, J Sunseri, A Jia… - Journal of chemical …, 2020 - ACS Publications
One of the main challenges in drug discovery is predicting protein–ligand binding affinity.
Recently, machine learning approaches have made substantial progress on this task …
Recently, machine learning approaches have made substantial progress on this task …