AIMC Topic: Drug Discovery
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Predicting blood-brain barrier permeability of molecules with a large language model and machine learning.
Scientific reports
Jul 9, 2024
Predicting the blood-brain barrier (BBB) permeability of small-molecule compounds using a novel artificial intelligence platform is necessary for drug discovery. Machine learning and a large language model on artificial intelligence (AI) tools improv...
Machine-Learning-Guided Peptide Drug Discovery: Development of GLP-1 Receptor Agonists with Improved Drug Properties.
Journal of medicinal chemistry
Jul 8, 2024
Peptide-based drug discovery has surged with the development of peptide hormone-derived analogs for the treatment of diabetes and obesity. Machine learning (ML)-enabled quantitative structure-activity relationship (QSAR) approaches have shown great p...
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...
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...
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 ...
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...
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...
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...
Harnessing the power of AI-based models to accelerate drug discovery against immune diseases.
Expert review of clinical immunology
Jul 1, 2024
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...
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