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
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
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
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...

Triple-Action Therapy: Combining Machine Learning, Docking, and Dynamics to Combat BRCA1-Mutated Breast Cancer.

Molecular biotechnology
Breast cancer dominates women's mortality, and among other factors, mutations in the BRCA1 gene are significant risk factors. Several approaches are followed to treat the BRCA1 affected cancer patients. However, specific BRCA1 inhibitors are not avai...

Integrated machine learning and physics-based methods assisted de novo design of Fatty Acyl-CoA synthase inhibitors.

Expert opinion on drug discovery
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
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
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
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
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...