Generative chemistry: drug discovery with deep learning generative models
The de novo design of molecular structures using deep learning generative models
introduces an encouraging solution to drug discovery in the face of the continuously …
introduces an encouraging solution to drug discovery in the face of the continuously …
[HTML][HTML] Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition
S Raschka, B Kaufman - Methods, 2020 - Elsevier
In the last decade, machine learning and artificial intelligence applications have received a
significant boost in performance and attention in both academic research and industry. The …
significant boost in performance and attention in both academic research and industry. The …
GLOW: A workflow integrating Gaussian-accelerated molecular dynamics and deep learning for free energy profiling
We introduce a Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and
free energy profiling workflow (GLOW) to predict molecular determinants and map free …
free energy profiling workflow (GLOW) to predict molecular determinants and map free …
PASSer: Prediction of allosteric sites server
Allostery is considered important in regulating protein's activity. Drug development depends
on the understanding of allosteric mechanisms, especially the identification of allosteric …
on the understanding of allosteric mechanisms, especially the identification of allosteric …
Harnessing reversed allosteric communication: a novel strategy for allosteric drug discovery
J Fan, Y Liu, R Kong, D Ni, Z Yu, S Lu… - Journal of Medicinal …, 2021 - ACS Publications
Allostery is a fundamental and extensive mechanism of intramolecular signal transmission.
Allosteric drugs possess several unique pharmacological advantages over traditional …
Allosteric drugs possess several unique pharmacological advantages over traditional …
Deep convolutional generative adversarial network (dcGAN) models for screening and design of small molecules targeting cannabinoid receptors
A deep convolutional generative adversarial network (dcGAN) model was developed in this
study to screen and design target-specific novel compounds for cannabinoid receptors. In …
study to screen and design target-specific novel compounds for cannabinoid receptors. In …
Drug design targeting active posttranslational modification protein isoforms
Modern drug design aims to discover novel lead compounds with attractable chemical
profiles to enable further exploration of the intersection of chemical space and biological …
profiles to enable further exploration of the intersection of chemical space and biological …
PASSer2. 0: accurate prediction of protein allosteric sites through automated machine learning
Allostery is a fundamental process in regulating protein activities. The discovery, design, and
development of allosteric drugs demand better identification of allosteric sites. Several …
development of allosteric drugs demand better identification of allosteric sites. Several …
[HTML][HTML] ALPACA: A machine Learning Platform for Affinity and selectivity profiling of CAnnabinoids receptors modulators
The development of small molecules that selectively target the cannabinoid receptor
subtype 2 (CB2R) is emerging as an intriguing therapeutic strategy to treat …
subtype 2 (CB2R) is emerging as an intriguing therapeutic strategy to treat …
Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors
A generative adversarial autoencoder for the rational design of potential HIV-1 entry
inhibitors able to block CD4-binding site of the viral envelope protein gp120 was developed …
inhibitors able to block CD4-binding site of the viral envelope protein gp120 was developed …