AIMC Topic: Drug Discovery

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Discovery of naturally inspired antimicrobial peptides using deep learning.

Bioorganic chemistry
Non-ribosomal peptides (NRPs) are promising lead compounds for novel antibiotics. Bioinformatic mining of silent microbial NRPS gene clusters provide crucial insights for the discovery and de novo design of bioactive peptides. Here, we describe the e...

Cyclic Peptide Therapeutic Agents Discovery: Computational and Artificial Intelligence-Driven Strategies.

Journal of medicinal chemistry
Cyclic peptides have emerged as promising modulators of protein-protein interactions due to their unique pharmacological properties and ability to target extensive flat binding interfaces. However, traditional strategies for developing cyclic peptide...

Computational Hit Finding: An Industry Perspective.

Journal of medicinal chemistry
Computational hit finding, particularly virtual screening, has been a mainstay of drug discovery campaigns for decades, providing a cost-efficient complement to wet experiments. Innovation in this space slowed considerably as these approaches converg...

Harnessing Medicinal Chemical Intuition from Collective Intelligence.

Journal of medicinal chemistry
Over the past decade, collective intelligence, i.e., the intelligence that emerges from collective efforts, has transformed complex problem-solving and decision-making. In drug discovery, decision-making often relies on medicinal chemistry intuition....

GICL: A Cross-Modal Drug Property Prediction Framework Based on Knowledge Enhancement of Large Language Models.

Journal of chemical information and modeling
Deep learning models have demonstrated their potential in learning effective molecular representations critical for drug property prediction and drug discovery. Despite significant advancements in leveraging multimodal drug molecule semantics, existi...

Comprehensive Drug-Likeness Prediction Using a Pretrained Transformer Model and Multitask Learning.

Journal of chemical information and modeling
Drug-likeness is essential in drug discovery, indicating the potential of a compound to become a successful therapeutic. However, existing rule-based and machine learning methods are limited by their reliance on hand-crafted features, poor generaliza...

Revolution of AAV in Drug Discovery: From Delivery System to Clinical Application.

Journal of medical virology
Adeno-associated virus (AAV) is a non-enveloped DNA virus infecting a wide variety of species, tissues, and cell types, which is recognized as a safe and effective method for delivering therapeutic transgenes. AAV vector is the most popular viral gen...

Advancements in Organoid-Based Drug Discovery: Revolutionizing Precision Medicine and Pharmacology.

Drug development research
Organoids, 3D cellular models derived from stem cells, have revolutionized drug testing by providing human-relevant systems for modeling diseases and testing drug efficacy. Unlike traditional 2D cell cultures or animal models, organoids closely resem...

Predicted and Explained: Transforming drug discovery with AI for high-precision receptor-ligand interaction modeling and binding analysis.

Computers in biology and medicine
The pharmaceutical industry faces persistent challenges in developing effective treatments for complex diseases, creating an urgent need for innovative approaches to accelerate drug discovery. A pivotal factor in this process is the accurate predicti...

MiRAGE-DTI: A novel approach for drug-target interaction prediction by integrating drug and target similarity metrics.

Computers in biology and medicine
MOTIVATION: Accurately predicting drug-target interactions (DTIs) is critical for accelerating drug discovery, repositioning, and development. Traditional experimental methods are often expensive and time-consuming, emphasizing the need for efficient...