AIMC Topic: Drug Discovery
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Artificial intelligence in central-peripheral interaction organ crosstalk: the future of drug discovery and clinical trials.
Pharmacological research
Apr 10, 2025
Drug discovery before the 20th century often focused on single genes, molecules, cells, or organs, failing to capture the complexity of biological systems. The emergence of protein-protein interaction network studies in 2001 marked a turning point an...
AI drug development's data problem.
Science (New York, N.Y.)
Apr 10, 2025
The future of drug discovery may be artificial intelligence (AI), but its present is not. AI is in its infancy in the field. To help AI mature, developers need nonproprietary, open, large, high-quality datasets to train and validate models, managed b...
Pre-trained molecular representations enable antimicrobial discovery.
Nature communications
Apr 10, 2025
The rise in antimicrobial resistance poses a worldwide threat, reducing the efficacy of common antibiotics. Determining the antimicrobial activity of new chemical compounds through experimental methods remains time-consuming and costly. While compoun...
PoseidonQ: A Free Machine Learning Platform for the Development, Analysis, and Validation of Efficient and Portable QSAR Models for Drug Discovery.
Journal of chemical information and modeling
Apr 9, 2025
The advent of powerful machine learning algorithms as well as the availability of high volume of pharmacological data has given new fuel to QSAR, opening new unprecedented options for deriving highly predictive models for assisting the rationale desi...
Unified Deep Learning of Molecular and Protein Language Representations with T5ProtChem.
Journal of chemical information and modeling
Apr 8, 2025
Deep learning has revolutionized difficult tasks in chemistry and biology, yet existing language models often treat these domains separately, relying on concatenated architectures and independently pretrained weights. These approaches fail to fully e...
Edge-enhanced interaction graph network for protein-ligand binding affinity prediction.
PloS one
Apr 8, 2025
Protein-ligand interactions are crucial in drug discovery. Accurately predicting protein-ligand binding affinity is essential for screening potential drugs. Graph neural networks have proven highly effective in modeling spatial relationships and thre...
Subtractive genomics approach: A guide to unveiling therapeutic targets across pathogens.
Journal of microbiological methods
Apr 7, 2025
Subtractive genomics is an adaptable bioinformatics technique that is used to identify potential therapeutic targets by differentiating essential genes in pathogens and non-pathogenic genes. Since, identification of therapeutic targets and understand...
Virtual reality in drug design: Benefits, applications and industrial perspectives.
Current opinion in structural biology
Apr 7, 2025
Virtual reality (VR) is a tool which has transformative potential in domains which involve the visualization of complex 3D data such as structure-based drug design (SBDD), where it offers new ways to visualize and manipulate complex molecular structu...
A deep learning model for structure-based bioactivity optimization and its application in the bioactivity optimization of a SARS-CoV-2 main protease inhibitor.
European journal of medicinal chemistry
Apr 5, 2025
Bioactivity optimization is a crucial and technical task in the early stages of drug discovery, traditionally carried out through iterative substituent optimization, a process that is often both time-consuming and expensive. To address this challenge...
Machine learning approaches enable the discovery of therapeutics across domains.
Molecular therapy : the journal of the American Society of Gene Therapy
Apr 3, 2025
Multi-modal datasets have grown exponentially in the last decade. This has created an enormous demand for machine learning models that can predict complex outcomes by leveraging cellular, molecular, and humoral profiles. Corresponding inference of me...
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