AI Medical Compendium Journal:
Journal of medicinal chemistry

Showing 1 to 10 of 54 articles

Application of Machine Learning and Mechanistic Modeling to Predict Intravenous Pharmacokinetic Profiles in Humans.

Journal of medicinal chemistry
Accurate prediction of new compounds' pharmacokinetic (PK) profile in humans is crucial for drug discovery. Traditional methods, including allometric scaling and mechanistic modeling, rely on parameters from or testing, which are labor-intensive an...

Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives.

Journal of medicinal chemistry
Drug discovery, a multifaceted process from compound identification to regulatory approval, historically plagued by inefficiencies and time lags due to limited data utilization, now faces urgent demands for accelerated lead compound identification. I...

Artificial Intelligence in Retrosynthesis Prediction and its Applications in Medicinal Chemistry.

Journal of medicinal chemistry
Retrosynthesis is a strategy to analyze the synthetic routes for target molecules in medicinal chemistry. However, traditional retrosynthesis predictions performed by chemists and rule-based expert systems struggle to adapt to the vast chemical space...

F-CPI: A Multimodal Deep Learning Approach for Predicting Compound Bioactivity Changes Induced by Fluorine Substitution.

Journal of medicinal chemistry
Fluorine (F) substitution is a common method of drug discovery and development. However, there are no accurate approaches available for predicting the bioactivity changes after F-substitution, as the effect of substitution on the interactions between...

Toward Dose Prediction at Point of Design.

Journal of medicinal chemistry
Human dose prediction (HDP) is a useful tool for compound optimization in preclinical drug discovery. We describe here our exclusively in silico HDP strategy to triage compound designs for synthesis and experimental profiling. Our goal is a model tha...

MPCD: A Multitask Graph Transformer for Molecular Property Prediction by Integrating Common and Domain Knowledge.

Journal of medicinal chemistry
Molecular property prediction with deep learning often employs self-supervised learning techniques to learn common knowledge through masked atom prediction. However, the common knowledge gained by masked atom prediction dramatically differs from the ...

Enhancing the Predictive Power of Machine Learning Models through a Chemical Space Complementary DEL Screening Strategy.

Journal of medicinal chemistry
DNA-encoded library (DEL) technology is an effective method for small molecule drug discovery, enabling high-throughput screening against target proteins. While DEL screening produces extensive data, it can reveal complex patterns not easily recogniz...

Deep-Learning-Driven Discovery of SN3-1, a Potent NLRP3 Inhibitor with Therapeutic Potential for Inflammatory Diseases.

Journal of medicinal chemistry
The NLRP3 inflammasome plays a central role in the pathogenesis of various intractable human diseases, making it an urgent target for therapeutic intervention. Here, we report the development of SN3-1, a novel orally potent NLRP3 inhibitor, designed ...

GPCRSPACE: A New GPCR Real Expanded Library Based on Large Language Models Architecture and Positive Sample Machine Learning Strategies.

Journal of medicinal chemistry
The quest for novel therapeutics targeting G protein-coupled receptors (GPCRs), essential in numerous physiological processes, is crucial in drug discovery. Despite the abundance of GPCR-targeting drugs, many receptors lack selective modulators, indi...

Can Machine Learning Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?

Journal of medicinal chemistry
Despite implementing hundreds of strategies, cancer drug development suffers from a 95% failure rate over 30 years, with only 30% of approved cancer drugs extending patient survival beyond 2.5 months. Adding more criteria without eliminating nonessen...