AI Medical Compendium Topic:
Molecular Docking Simulation

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Boosting Docking-Based Virtual Screening with Deep Learning.

Journal of chemical information and modeling
In this work, we propose a deep learning approach to improve docking-based virtual screening. The deep neural network that is introduced, DeepVS, uses the output of a docking program and learns how to extract relevant features from basic data such as...

A Graph Approach to Mining Biological Patterns in the Binding Interfaces.

Journal of computational biology : a journal of computational molecular cell biology
Protein-RNA interactions play important roles in the biological systems. Searching for regular patterns in the Protein-RNA binding interfaces is important for understanding how protein and RNA recognize each other and bind to form a complex. Herein, ...

Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR.

Molecular informatics
Neural networks have generated valuable Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models for a wide variety of small molecules and materials properties. They have grown in sophistication and many of their initial problems hav...

Machine Learning Approaches for Predicting Protein Complex Similarity.

Journal of computational biology : a journal of computational molecular cell biology
Discriminating native-like structures from false positives with high accuracy is one of the biggest challenges in protein-protein docking. While there is an agreement on the existence of a relationship between various favorable intermolecular interac...

Sequence-Based Prediction of Protein-Carbohydrate Binding Sites Using Support Vector Machines.

Journal of chemical information and modeling
Carbohydrate-binding proteins play significant roles in many diseases including cancer. Here, we established a machine-learning-based method (called sequence-based prediction of residue-level interaction sites of carbohydrates, SPRINT-CBH) to predict...

Correcting the impact of docking pose generation error on binding affinity prediction.

BMC bioinformatics
BACKGROUND: Pose generation error is usually quantified as the difference between the geometry of the pose generated by the docking software and that of the same molecule co-crystallised with the considered protein. Surprisingly, the impact of this e...

A D3R prospective evaluation of machine learning for protein-ligand scoring.

Journal of computer-aided molecular design
We assess the performance of several machine learning-based scoring methods at protein-ligand pose prediction, virtual screening, and binding affinity prediction. The methods and the manner in which they were trained make them sufficiently diverse to...

CGBVS-DNN: Prediction of Compound-protein Interactions Based on Deep Learning.

Molecular informatics
Computational prediction of compound-protein interactions (CPIs) is of great importance for drug design as the first step in in-silico screening. We previously proposed chemical genomics-based virtual screening (CGBVS), which predicts CPIs by using a...

Effect of Binding Pose and Modeled Structures on SVMGen and GlideScore Enrichment of Chemical Libraries.

Journal of chemical information and modeling
Virtual screening consists of docking libraries of small molecules to a target protein followed by rank-ordering of the resulting structures using scoring functions. The ability of scoring methods to distinguish between actives and inactives depends ...

Machine learning optimization of cross docking accuracy.

Computational biology and chemistry
Performance of small molecule automated docking programs has conceptually been divided into docking -, scoring -, ranking - and screening power, which focuses on the crystal pose prediction, affinity prediction, ligand ranking and database screening ...