AIMC Topic: Drug Discovery
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GraphGIM: rethinking molecular graph contrastive learning via geometry image modeling.
BMC biology
Jul 1, 2025
BACKGROUND: Learning molecular representations is crucial for accurate drug discovery. Using graphs to represent molecules is a popular solution, and many researchers have used contrastive learning to improve the generalization of molecular graph rep...
BGATT-GR: accurate identification of glucocorticoid receptor antagonists based on data augmentation combined with BiGRU-attention.
Scientific reports
Jul 1, 2025
The glucocorticoid receptor (GR) is a critical nuclear receptor that regulates a broad spectrum of physiological functions, including stress adaptation, immune response, and metabolism. Given the association between aberrant GR signaling and various ...
Discovery of SARS-CoV-2 papain-like protease inhibitors through machine learning and molecular simulation approaches.
Drug discoveries & therapeutics
Jun 27, 2025
The papain-like protease (PLpro), a cysteine protease found in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), plays a crucial role in viral replication by cleaving the viral polyproteins and interfering with the host's innate immune re...
AI-driven discovery of brain-penetrant Galectin-3 inhibitors for Alzheimer's disease therapy.
Pharmacological research
Jun 19, 2025
Galectin-3 (Gal-3) has emerged as a critical regulator of neuroinflammation and a promising therapeutic target for Alzheimer's disease (AD). Nevertheless, the development of brain-penetrant small-molecule Gal-3 inhibitors poses a significant challeng...
Molecular dynamics-driven drug discovery.
Physical chemistry chemical physics : PCCP
Jun 18, 2025
Molecular dynamics (MD) simulation is an important tool and has a wide range of applications in many scientific fields, including drug discovery. Herein, focusing on drug discovery, the early compound discovery stage in particular, we discuss some of...
Improved Prediction of Drug-Protein Interactions through Physics-Based Few-Shot Learning.
Journal of chemical information and modeling
Jun 17, 2025
Accurate prediction of drug-protein interactions is crucial for drug discovery. Due to the bottleneck of traditional scoring functions, many machine learning scoring functions (MLSFs) have been proposed for structure-based drug screening. However, ex...
LumiCharge: Spherical Harmonic Convolutional Networks for Atomic Charge Prediction in Drug Discovery.
The journal of physical chemistry letters
Jun 16, 2025
Atomic charge is crucial in drug design for analyzing reactive sites and interactions between ligands and targets. While quantum mechanical methods offer high accuracy, they are generally computationally costly. Conversely, empirical approaches, whil...
Discovery of Novel Anti-Acetylcholinesterase Peptides Using a Machine Learning and Molecular Docking Approach.
Drug design, development and therapy
Jun 14, 2025
OBJECTIVE: Alzheimer's disease poses a significant threat to human health. Currenttherapeutic medicines, while alleviate symptoms, fail to reverse the disease progression or reduce its harmful effects, and exhibit toxicity and side effects such as ga...
Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries.
Chemical reviews
Jun 6, 2025
The nexus of quantum computing and machine learning─quantum machine learning─offers the potential for significant advancements in chemistry. This Review specifically explores the potential of quantum neural networks on gate-based quantum computers wi...
The latest advances with natural products in drug discovery and opportunities for the future: a 2025 update.
Expert opinion on drug discovery
Jun 5, 2025
INTRODUCTION: The landscape of drug discovery is rapidly evolving, with natural products (NPs) playing a pivotal role in the development of novel therapeutics. Despite their historical significance, challenges persist in fully harnessing their potent...
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