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Construction of a Deep Neural Network Energy Function for Protein Physics.

Journal of chemical theory and computation
The traditional approach of computational biology consists of calculating molecule properties by using approximate classical potentials. Interactions between atoms are described by an energy function derived from physical principles or fitted to expe...

Biomedical Data and Deep Learning Computational Models for Predicting Compound-Protein Relations.

IEEE/ACM transactions on computational biology and bioinformatics
The identification of compound-protein relations (CPRs), which includes compound-protein interactions (CPIs) and compound-protein affinities (CPAs), is critical to drug development. A common method for compound-protein relation identification is the ...

Inferring Drug-Target Interactions Based on Random Walk and Convolutional Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics
Computational strategies for identifying new drug-target interactions (DTIs) can guide the process of drug discovery, reduce the cost and time of drug development, and thus promote drug development. Most recently proposed methods predict DTIs via int...

I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction.

Nature protocols
Most proteins in cells are composed of multiple folding units (or domains) to perform complex functions in a cooperative manner. Relative to the rapid progress in single-domain structure prediction, there are few effective tools available for multi-d...

ppdx: Automated modeling of protein-protein interaction descriptors for use with machine learning.

Journal of computational chemistry
This paper describes ppdx, a python workflow tool that combines protein sequence alignment, homology modeling, and structural refinement, to compute a broad array of descriptors for characterizing protein-protein interactions. The descriptors can be ...

A deep learning framework for identifying essential proteins based on multiple biological information.

BMC bioinformatics
BACKGROUND: Essential Proteins are demonstrated to exert vital functions on cellular processes and are indispensable for the survival and reproduction of the organism. Traditional centrality methods perform poorly on complex protein-protein interacti...

DCSE:Double-Channel-Siamese-Ensemble model for protein protein interaction prediction.

BMC genomics
BACKGROUND: Protein-protein interaction (PPI) is very important for many biochemical processes. Therefore, accurate prediction of PPI can help us better understand the role of proteins in biochemical processes. Although there are many methods to pred...

DeepRHD: An efficient hybrid feature extraction technique for protein remote homology detection using deep learning strategies.

Computational biology and chemistry
In computational biology, the Protein Remote homology Detection technique (PRHD) has got undeniable significance. It is mostly important for structure and function identification of a protein sequence. The previous years have seen a challenge that la...

Characterizing Metastable States with the Help of Machine Learning.

Journal of chemical theory and computation
Present-day atomistic simulations generate long trajectories of ever more complex systems. Analyzing these data, discovering metastable states, and uncovering their nature are becoming increasingly challenging. In this paper, we first use the variati...

Intermittent fasting-induced biomolecular modifications in rat tissues detected by ATR-FTIR spectroscopy and machine learning algorithms.

Analytical biochemistry
This study aimed to reveal the intermittent fasting-induced alterations in biomolecules of the liver, ileum, and colon tissues of rats using Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) algorithms developed on infrared spectroc...