AIMC Topic: Proteins

Clear Filters Showing 1871 to 1880 of 2080 articles

MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm.

Briefings in bioinformatics
Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and ...

Computationally predicting binding affinity in protein-ligand complexes: free energy-based simulations and machine learning-based scoring functions.

Briefings in bioinformatics
Accurately predicting protein-ligand binding affinities can substantially facilitate the drug discovery process, but it remains as a difficult problem. To tackle the challenge, many computational methods have been proposed. Among these methods, free ...

The impact of compound library size on the performance of scoring functions for structure-based virtual screening.

Briefings in bioinformatics
Larger training datasets have been shown to improve the accuracy of machine learning (ML)-based scoring functions (SFs) for structure-based virtual screening (SBVS). In addition, massive test sets for SBVS, known as ultra-large compound libraries, ha...

Improving structure-based virtual screening performance via learning from scoring function components.

Briefings in bioinformatics
Scoring functions (SFs) based on complex machine learning (ML) algorithms have gradually emerged as a promising alternative to overcome the weaknesses of classical SFs. However, extensive efforts have been devoted to the development of SFs based on n...

MDeePred: novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery.

Bioinformatics (Oxford, England)
MOTIVATION: Identification of interactions between bioactive small molecules and target proteins is crucial for novel drug discovery, drug repurposing and uncovering off-target effects. Due to the tremendous size of the chemical space, experimental b...

Learned Embeddings from Deep Learning to Visualize and Predict Protein Sets.

Current protocols
Models from machine learning (ML) or artificial intelligence (AI) increasingly assist in guiding experimental design and decision making in molecular biology and medicine. Recently, Language Models (LMs) have been adapted from Natural Language Proces...

Text mining for modeling of protein complexes enhanced by machine learning.

Bioinformatics (Oxford, England)
MOTIVATION: Procedures for structural modeling of protein-protein complexes (protein docking) produce a number of models which need to be further analyzed and scored. Scoring can be based on independently determined constraints on the structure of th...

GraphQA: protein model quality assessment using graph convolutional networks.

Bioinformatics (Oxford, England)
MOTIVATION: Proteins are ubiquitous molecules whose function in biological processes is determined by their 3D structure. Experimental identification of a protein's structure can be time-consuming, prohibitively expensive and not always possible. Alt...

Secondary structure prediction of protein based on multi scale convolutional attention neural networks.

Mathematical biosciences and engineering : MBE
To fully extract the local and long-range information of amino acid sequences and enhance the effective information, this research proposes a secondary structure prediction model of protein based on a multi-scale convolutional attentional neural netw...

DeepPurpose: a deep learning library for drug-target interaction prediction.

Bioinformatics (Oxford, England)
SUMMARY: Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scien...