AIMC Topic: Molecular Docking Simulation
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Potential impact of organophosphate esters on thyroid eye disease based on machine learning and molecular docking.
The Science of the total environment
Dec 3, 2024
Organophosphate esters (OPEs) are widely used as flame retardants and plasticizers in daily commodities and building materials. Some OPEs, acting as agonists of the thyroid-stimulating hormone receptor (TSHR), may contribute to the development of thy...
SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction.
Nature methods
Nov 27, 2024
Accurately predicting protein-ligand interactions is crucial for understanding cellular processes. We introduce SurfDock, a deep-learning method that addresses this challenge by integrating protein sequence, three-dimensional structural graphs and su...
Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation.
Journal of chemical information and modeling
Nov 27, 2024
Cyanobacteria strains have the potential to produce bioactive compounds that can be used in therapeutics and bioremediation. Therefore, compiling all information about these compounds to consider their value as bioresources for industrial and researc...
Integrated machine learning and physics-based methods assisted de novo design of Fatty Acyl-CoA synthase inhibitors.
Expert opinion on drug discovery
Nov 25, 2024
BACKGROUND: Tuberculosis is an infectious disease that has become endemic worldwide. The causative bacteria (Mtb) is targeted via several exciting drug targets. One newly discovered target is the Fatty Acyl-CoA synthase, which plays a significant ro...
Improved Prediction of Ligand-Protein Binding Affinities by Meta-modeling.
Journal of chemical information and modeling
Nov 22, 2024
The accurate screening of candidate drug ligands against target proteins through computational approaches is of prime interest to drug development efforts. Such virtual screening depends in part on methods to predict the binding affinity between liga...
ClickGen: Directed exploration of synthesizable chemical space via modular reactions and reinforcement learning.
Nature communications
Nov 22, 2024
Despite the significant potential of generative models, low synthesizability of many generated molecules limits their real-world applications. In response to this issue, we develop ClickGen, a deep learning model that utilizes modular reactions like ...
A Machine Learning-Based Approach for the Prediction of Anticoagulant Activity of Hypericum perforatum L. and Evaluation of Compound Activity.
Phytochemical analysis : PCA
Nov 17, 2024
INTRODUCTION: Hypericum perforatum L. (HPL) is extensively researched domestically and internationally as a medicinal plant. However, no reports of studies related to the anticoagulant activity of HPL have been retrieved. The specific bioactive compo...
Deep learning pipeline for accelerating virtual screening in drug discovery.
Scientific reports
Nov 16, 2024
In the race to combat ever-evolving diseases, the drug discovery process often faces the hurdles of high-cost and time-consuming procedures. To tackle these challenges and enhance the efficiency of identifying new therapeutic agents, we introduce Vir...
Small Molecule Inhibitors of Topoisomerase I Identified by Machine Learning and In Vitro Assays.
International journal of molecular sciences
Nov 15, 2024
Tuberculosis (TB) caused by is a leading infectious cause of death globally. The treatment of patients becomes much more difficult for the increasingly common multi-drug resistant TB. Topoisomerase I is essential for the viability of and has been v...
Target Fisher: A Consensus Structure-Based Target Prediction Tool, and its Application in the Discovery of Selective MAO-B Inhibitors.
Chemistry (Weinheim an der Bergstrasse, Germany)
Nov 14, 2024
In this work we introduce Target Fisher, a consensus structure-based target prediction tool that integrates molecular docking and machine learning with the aim to aid in the identification of potential biological targets and the optimization of the u...
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