AIMC Topic: Drug Discovery

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Versatile Framework for Drug-Target Interaction Prediction by Considering Domain-Specific Features.

Journal of chemical information and modeling Jul 8, 2024
Predicting drug-target interactions (DTIs) is one of the crucial tasks in drug discovery, but traditional wet-lab experiments are costly and time-consuming. Recently, deep learning has emerged as a promising tool for accelerating DTI prediction due t...
Proteins Protein Domains Pharmaceutical Preparations Ligands Drug Discovery Deep Learning
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A data science roadmap for open science organizations engaged in early-stage drug discovery.

Nature communications Jul 5, 2024
The Structural Genomics Consortium is an international open science research organization with a focus on accelerating early-stage drug discovery, namely hit discovery and optimization. We, as many others, believe that artificial intelligence (AI) is...
Drug Discovery Machine Learning Data Science Databases, Factual Humans Data Mining Artificial Intelligence Information Dissemination Cloud Computing
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Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehensive review.

Computers in biology and medicine Jul 3, 2024
Artificial intelligence (AI) has played a vital role in computer-aided drug design (CADD). This development has been further accelerated with the increasing use of machine learning (ML), mainly deep learning (DL), and computing hardware and software ...
Deep Learning Machine Learning Drug Discovery Humans Artificial Intelligence
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Docking Score ML: Target-Specific Machine Learning Models Improving Docking-Based Virtual Screening in 155 Targets.

Journal of chemical information and modeling Jul 3, 2024
In drug discovery, molecular docking methods face challenges in accurately predicting energy. Scoring functions used in molecular docking often fail to simulate complex protein-ligand interactions fully and accurately leading to biases and inaccuraci...
Molecular Docking Simulation Drug Discovery Antineoplastic Agents Humans Machine Learning User-Computer Interface Drug Evaluation, Preclinical Proteins Ligands
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Deriving general structure-activity/selectivity relationship patterns for different subfamilies of cyclin-dependent kinase inhibitors using machine learning methods.

Scientific reports Jul 3, 2024
Cyclin-dependent kinases (CDKs) play essential roles in regulating the cell cycle and are among the most critical targets for cancer therapy and drug discovery. The primary objective of this research is to derive general structure-activity relationsh...
Protein Kinase Inhibitors Cyclin-Dependent Kinases Drug Discovery Structure-Activity Relationship Humans Neural Networks, Computer Machine Learning
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A Hierarchical Graph Neural Network Framework for Predicting Protein-Protein Interaction Modulators With Functional Group Information and Hypergraph Structure.

IEEE journal of biomedical and health informatics Jul 2, 2024
Accurate prediction of small molecule modulators targeting protein-protein interactions (PPIMs) remains a significant challenge in drug discovery. Existing machine learning-based models rely on manual feature engineering, which is tedious and task-sp...
Algorithms Computational Biology Proteins Deep Learning Neural Networks, Computer Drug Discovery Protein Interaction Mapping Protein Interaction Maps
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Harnessing the power of AI-based models to accelerate drug discovery against immune diseases.

Expert review of clinical immunology Jul 1, 2024
Humans Immune System Diseases Artificial Intelligence Animals Drug Discovery
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DrugFlow: An AI-Driven One-Stop Platform for Innovative Drug Discovery.

Journal of chemical information and modeling Jun 26, 2024
Artificial intelligence (AI)-aided drug design has demonstrated unprecedented effects on modern drug discovery, but there is still an urgent need for user-friendly interfaces that bridge the gap between these sophisticated tools and scientists, parti...
Molecular Docking Simulation Cloud Computing Software Drug Design Humans Drug Discovery Algorithms Artificial Intelligence Quantitative Structure-Activity Relationship
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The Role of AI in Drug Discovery.

Chembiochem : a European journal of chemical biology Jun 26, 2024
The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This revie...
Drug Discovery Drug Repositioning Humans Artificial Intelligence Drug Design
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Quantum-Informed Molecular Representation Learning Enhancing ADMET Property Prediction.

Journal of chemical information and modeling Jun 25, 2024
We examined pretraining tasks leveraging abundant labeled data to effectively enhance molecular representation learning in downstream tasks, specifically emphasizing graph transformers to improve the prediction of ADMET properties. Our investigation ...
Machine Learning Drug Discovery Humans Pharmaceutical Preparations Quantum Theory
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