AIMC Topic: Drug Discovery

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MolAI: A Deep Learning Framework for Data-Driven Molecular Descriptor Generation and Advanced Drug Discovery Applications.

Journal of chemical information and modeling Sep 15, 2025
This study introduces MolAI, a robust deep learning model designed for data-driven molecular descriptor generation. Utilizing a vast training data set of 221 million unique compounds, MolAI employs an autoencoder neural machine translation model to g...
Deep Learning Structure-Activity Relationship Ligands Neural Networks, Computer Drug Discovery
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BIOPTIC B1 Ultra-High-Throughput Virtual Screening System Discovers LRRK2 Ligands in Vast Chemical Space.

Journal of chemical information and modeling Sep 12, 2025
The rapid expansion of chemical space presents significant challenges in identifying novel ligands for drug targets. Here, we introduce BIOPTIC B1, an ultra-high-throughput ligand-based virtual screening system capable of rapidly evaluating multi-bil...
Humans Drug Discovery High-Throughput Screening Assays Small Molecule Libraries User-Computer Interface Ligands Leucine-Rich Repeat Serine-Threonine Protein Kinase-2
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MOLECULE: Molecular-dynamics and Optimized deep Learning for Entropy-regularized Classification and Uncertainty-aware Ligand Evaluation.

Journal of chemical theory and computation Sep 11, 2025
Machine learning (ML) and deep learning (DL) methodologies have significantly advanced drug discovery and design in several aspects. Additionally, the integration of structure-based data has proven to successfully support and improve the models' pred...
Protein Kinase Inhibitors Entropy Uncertainty Molecular Dynamics Simulation Ligands Deep Learning Drug Discovery
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Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery.

Journal of chemical information and modeling Sep 11, 2025
Machine Learning (ML) methods that relate molecular structure to properties are frequently proposed as in silico surrogates for expensive or time-consuming experiments. In small molecule drug discovery, such methods inform high-stakes decisions like ...
Drug Discovery Small Molecule Libraries Machine Learning
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Exploring the Frontiers of Computational NMR: Methods, Applications, and Challenges.

Chemical reviews Sep 9, 2025
Computational methods have revolutionized NMR spectroscopy, driving significant advancements in structural biology and related fields. This review focuses on recent developments in quantum chemical and machine learning approaches for computational NM...
Proteins Quantum Theory Drug Discovery Magnetic Resonance Spectroscopy Humans Metabolomics Machine Learning
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HPDAF: A practical tool for predicting drug-target binding affinity using multimodal features.

European journal of medicinal chemistry Sep 8, 2025
Accurate prediction of drug-target binding affinity is crucial for efficient drug discovery and design, enabling researchers to better understand molecular interactions and accelerate the identification of promising drug candidates. Despite recent ad...
Deep Learning Protein Binding Drug Design Drug Discovery Humans Proteins Pharmaceutical Preparations Binding Sites Molecular Structure
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KGG: Knowledge-Guided Graph Self-Supervised Learning to Enhance Molecular Property Predictions.

Journal of chemical information and modeling Sep 7, 2025
Molecular property prediction has become essential in accelerating advancements in drug discovery and materials science. Graph Neural Networks have recently demonstrated remarkable success in molecular representation learning; however, their broader ...
Neural Networks, Computer Drug Discovery Supervised Machine Learning
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All That Glitters Is Not Gold: Importance of Rigorous Evaluation of Proteochemometric Models.

Journal of chemical information and modeling Sep 3, 2025
Proteochemometric models (PCMs) are used in computational drug discovery to employ both protein and ligand representations jointly for bioactivity prediction. While machine learning (ML) and deep learning (DL) have come to dominate PCMs, often servin...
Ligands Deep Learning Machine Learning Drug Discovery Proteins
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In Search of Beautiful Molecules: A Perspective on Generative Modeling for Drug Design.

Journal of chemical information and modeling Sep 2, 2025
Generative modeling with artificial intelligence (GenAI) offers an emerging approach to discover novel, efficacious, and safe drugs by enabling the systematic exploration of chemical space and to design molecules that are synthesizable while also hav...
Humans Drug Discovery Artificial Intelligence Drug Design
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From AI-Driven Sequence Generation to Molecular Simulation: A Comprehensive Framework for Antimicrobial Peptide Discovery.

Journal of chemical information and modeling Aug 29, 2025
Antimicrobial Peptides (AMPs) are a promising strategy to address bacterial resistance, yet only a limited number have advanced to clinical trials. Recent advances in deep learning provide new opportunities for AMP design. Here, we propose an integra...
Antimicrobial Peptides Microbial Sensitivity Tests Amino Acid Sequence Drug Discovery Escherichia coli Deep Learning Staphylococcus aureus Anti-Bacterial Agents Molecular Dynamics Simulation
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