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Artificial Intelligence for Predicting Small-Molecule Bioactive Conformations.

Yuanchen Liu¹, Suya Chen¹, Kejiang Lin¹

¹Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China.

Journal of Chemical Information and Modeling

|April 16, 2026

Summary

Artificial intelligence (AI) models are revolutionizing drug discovery by accurately predicting bioactive conformations, overcoming limitations of traditional methods. This review explores AI advancements, challenges, and future directions for functional molecular design.

Keywords:

bioactive conformation diffusion models drug discovery generative AI geometric deep learning rotational equivariance

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Published on: November 3, 2011

Area of Science:

Computational chemistry
Drug discovery
Artificial intelligence in pharmacology

Background:

Predicting bioactive conformations is crucial for drug discovery but challenging for traditional methods assuming global minimum energy states.
Data-driven artificial intelligence (AI) models offer a new paradigm by learning conformational probability distributions for high-precision structure prediction.

Purpose of the Study:

To systematically review AI methodologies for bioactive conformation prediction.
To analyze AI model effectiveness in benchmarks and de novo design.
To discuss challenges and future outlook in AI-driven drug discovery.

Main Methods:

Survey of geometric regression, generative, and conditional generative AI models.
Analysis of AI model performance in established benchmarks.
Evaluation of AI in de novo molecular design experiments.

Main Results:

AI models demonstrate significant potential for high-precision bioactive conformation prediction.
Review covers diverse AI techniques and their applications in drug design.
Identified key challenges include data sparsity, sampling efficiency, and interpretability.

Conclusions:

AI is transforming drug discovery by enabling accurate prediction of bioactive conformations.
Addressing challenges in data, algorithms, and human-AI collaboration is key for future progress.
Bridging the gap from structural prediction to functional molecular design is the next frontier.