AIMC Topic: Molecular Docking Simulation

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MILCDock: Machine Learning Enhanced Consensus Docking for Virtual Screening in Drug Discovery.

Journal of chemical information and modeling
Molecular docking tools are regularly used to computationally identify new molecules in virtual screening for drug discovery. However, docking tools suffer from inaccurate scoring functions with widely varying performance on different proteins. To en...

Deep learning drives efficient discovery of novel antihypertensive peptides from soybean protein isolate.

Food chemistry
As a potential and effective substitute for the drugs of antihypertension, the food-derived antihypertensive peptides have arisen great interest in scholars recently. However, the traditional screening methods for antihypertensive peptides are at con...

Development of a deep learning-based quantitative structure-activity relationship model to identify potential inhibitors against the 3C-like protease of SARS-CoV-2.

Future medicinal chemistry
In the recent COVID-19 pandemic, SARS-CoV-2 infection spread worldwide. TheĀ 3C-like protease (3CLpro) is a promising drug target for SARS-CoV-2. We constructed a deep learning-based convolutional neural network-quantitative structure-activity relat...

DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features.

Journal of chemical information and modeling
Computational methods for virtual screening can dramatically accelerate early-stage drug discovery by identifying potential hits for a specified target. Docking algorithms traditionally use physics-based simulations to address this challenge by estim...

Systematic Improvement of the Performance of Machine Learning Scoring Functions by Incorporating Features of Protein-Bound Water Molecules.

Journal of chemical information and modeling
Water molecules at the ligand-protein interfaces play crucial roles in the binding of the ligands, but the behavior of protein-bound water is largely ignored in many currently used machine learning (ML)-based scoring functions (SFs). In an attempt to...

PyPLIF HIPPOS and Receptor Ensemble Docking Increase the Prediction Accuracy of the Structure-Based Virtual Screening Protocol Targeting Acetylcholinesterase.

Molecules (Basel, Switzerland)
In this article, the upgrading process of the structure-based virtual screening (SBVS) protocol targeting acetylcholinesterase (AChE) previously published in 2017 is presented. The upgraded version of PyPLIF called PyPLIF HIPPOS and the receptor ense...

Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model.

Biomolecules
The outbreak of COVID-19 caused millions of deaths worldwide, and the number of total infections is still rising. It is necessary to identify some potentially effective drugs that can be used to prevent the development of severe symptoms, or even dea...

GraphSite: Ligand Binding Site Classification with Deep Graph Learning.

Biomolecules
The binding of small organic molecules to protein targets is fundamental to a wide array of cellular functions. It is also routinely exploited to develop new therapeutic strategies against a variety of diseases. On that account, the ability to effect...

SCORCH: Improving structure-based virtual screening with machine learning classifiers, data augmentation, and uncertainty estimation.

Journal of advanced research
INTRODUCTION: The discovery of a new drug is a costly and lengthy endeavour. The computational prediction of which small molecules can bind to a protein target can accelerate this process if the predictions are fast and accurate enough. Recent machin...

DeepBindBC: A practical deep learning method for identifying native-like protein-ligand complexes in virtual screening.

Methods (San Diego, Calif.)
Identifying native-like protein-ligand complexes (PLCs) from an abundance of docking decoys is critical for large-scale virtual drug screening in early-stage drug discovery lead searching efforts. Providing reliable prediction is still a challenge fo...