AIMC Topic: Molecular Docking Simulation

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Using diverse potentials and scoring functions for the development of improved machine-learned models for protein-ligand affinity and docking pose prediction.

Journal of computer-aided molecular design
The advent of computational drug discovery holds the promise of significantly reducing the effort of experimentalists, along with monetary cost. More generally, predicting the binding of small organic molecules to biological macromolecules has far-re...

Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors.

International journal of molecular sciences
Olfactory receptors (ORs) constitute the largest superfamily of G protein-coupled receptors (GPCRs). ORs are involved in sensing odorants as well as in other ectopic roles in non-nasal tissues. Matching of an enormous number of the olfactory stimulat...

De novo design of novel protease inhibitor candidates in the treatment of SARS-CoV-2 using deep learning, docking, and molecular dynamic simulations.

Computers in biology and medicine
The main protease of SARS-CoV-2 is a critical target for the design and development of antiviral drugs. 2.5 M compounds were used in this study to train an LSTM generative network via transfer learning in order to identify the four best candidates ca...

3D-Scaffold: A Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds.

The journal of physical chemistry. B
The prerequisite of therapeutic drug design and discovery is to identify novel molecules and developing lead candidates with desired biophysical and biochemical properties. Deep generative models have demonstrated their ability to find such molecules...

Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning.

Journal of chemical information and modeling
One of the main challenges of structure-based virtual screening (SBVS) is the incorporation of the receptor's flexibility, as its explicit representation in every docking run implies a high computational cost. Therefore, a common alternative to inclu...

Computational investigation of drug bank compounds against 3C-like protease (3CL) of SARS-CoV-2 using deep learning and molecular dynamics simulation.

Molecular diversity
Blocking the main replicating enzyme, 3 Chymotrypsin-like protease (3CL) is the most promising drug development strategy against the SARS-CoV-2 virus, responsible for the current COVID-19 pandemic. In the present work, 9101 drugs obtained from the dr...

Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking.

Journal of chemical information and modeling
Here, we report the implementation and application of a simple, structure-aware framework to generate target-specific screening libraries. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking t...

Guided structure-based ligand identification and design via artificial intelligence modeling.

Expert opinion on drug discovery
INTRODUCTION: The implementation of Artificial Intelligence (AI) methodologies to drug discovery (DD) are on the rise. Several applications have been developed for structure-based DD, where AI methods provide an alternative framework for the identifi...

FFENCODER-PL: Pair Wise Energy Descriptors for Protein-Ligand Pose Selection.

Journal of chemical theory and computation
Scoring functions are the essential component in molecular docking methods. An accurate scoring function is expected to distinguish the native ligand pose from decoy poses. Our previous experience (Pei et al. 2019, 59 (7), 3305-3315) proved that com...

Machine-learning methods for ligand-protein molecular docking.

Drug discovery today
Artificial intelligence (AI) is often presented as a new Industrial Revolution. Many domains use AI, including molecular simulation for drug discovery. In this review, we provide an overview of ligand-protein molecular docking and how machine learnin...