AIMC Topic: Drug Discovery

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Advances in artificial intelligence for drug delivery and development: A comprehensive review.

Computers in biology and medicine
Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on dr...

Discovery of a Novel and Potent LCK Inhibitor for Leukemia Treatment via Deep Learning and Molecular Docking.

Journal of chemical information and modeling
The lymphocyte-specific protein tyrosine kinase (LCK) plays a crucial role in both T-cell development and activation. Dysregulation of LCK signaling has been demonstrated to drive the oncogenesis of T-cell acute lymphoblastic leukemia (T-ALL), thus p...

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

Molecules (Basel, Switzerland)
The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of ...

Synergistic Machine Learning Accelerated Discovery of Nanoporous Inorganic Crystals as Non-Absorbable Oral Drugs.

Advanced materials (Deerfield Beach, Fla.)
Machine learning (ML) has taken drug discovery to new heights, where effective ML training requires vast quantities of high-quality experimental data as input. Non-absorbable oral drugs (NODs) have unique safety advantage for chronic diseases due to ...

TumFlow: An AI Model for Predicting New Anticancer Molecules.

International journal of molecular sciences
Melanoma is the fifth most common cancer in the United States. Conventional drug discovery methods are inherently time-consuming and costly, which imposes significant limitations. However, the advent of Artificial Intelligence (AI) has opened up new ...

Artificial Intelligence in Drug Identification and Validation: A Scoping Review.

Drug research
The end-to-end process in the discovery of drugs involves therapeutic candidate identification, validation of identified targets, identification of hit compound series, lead identification and optimization, characterization, and formulation and devel...

Equivariant score-based generative diffusion framework for 3D molecules.

BMC bioinformatics
BACKGROUND: Molecular biology is crucial for drug discovery, protein design, and human health. Due to the vastness of the drug-like chemical space, depending on biomedical experts to manually design molecules is exceedingly expensive. Utilizing gener...

PfgPDI: Pocket feature-enabled graph neural network for protein-drug interaction prediction.

Journal of bioinformatics and computational biology
Biomolecular interaction recognition between ligands and proteins is an essential task, which largely enhances the safety and efficacy in drug discovery and development stage. Studying the interaction between proteins and ligands can improve the unde...

Artificial intelligence for high content imaging in drug discovery.

Current opinion in structural biology
Artificial intelligence (AI) and high-content imaging (HCI) are contributing to advancements in drug discovery, propelled by the recent progress in deep neural networks. This review highlights AI's role in analysis of HCI data from fixed and live-cel...

Structure-aware machine learning strategies for antimicrobial peptide discovery.

Scientific reports
Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a p...