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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

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Computational and AI-Driven Ecosystem for Structure-Based Covalent Drug Discovery.

Shi Li¹, Hongyan Du¹, Xujun Zhang¹

¹College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.

Accounts of Chemical Research

|February 27, 2026

Summary

This study introduces an integrated computational ecosystem for covalent drug discovery, leveraging artificial intelligence (AI) and deep learning (DL) to accelerate the development of new therapies. The system connects databases, predictive models, and experimental feedback for efficient drug design.

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Area of Science:

Computational chemistry and cheminformatics
Drug discovery and medicinal chemistry
Artificial intelligence in drug development

Background:

Covalent drugs are increasingly important, with over 125 approved by the FDA.
Deep learning (DL) and artificial intelligence (AI) are transforming drug discovery.
An integrated computational ecosystem is crucial for realizing the potential of AI in covalent drug development.

Purpose of the Study:

To describe a computational and AI-driven ecosystem for structure-based covalent drug discovery.
To highlight contributions to building such an ecosystem, linking databases, models, workflows, and experimental feedback.
To accelerate the development of next-generation covalent therapies by addressing challenges from site identification to lead discovery.

Main Methods:

Systematic collection and curation of covalent-relevant databases.
Development of AI/physics-based predictive and scoring models, including deep learning for molecular docking and site prediction.
Construction of interoperable computational workflows for virtual screening and lead optimization.
Implementation of closed-loop feedback integrating experimental outcomes to refine models and databases.

Main Results:

Demonstrated the potential of an integrated ecosystem for accelerating covalent drug discovery.
Showcased a case study using a virtual screening pipeline for covalent CRM1 inhibitors, bridging computational prediction and biological validation.
Provided insights into the performance and limitations of AI-driven docking algorithms, considering advancements like AlphaFold3.

Conclusions:

An integrated, data-driven ecosystem is essential for advancing covalent drug discovery.
AI and computational tools, when systematically applied, can significantly expedite the identification and optimization of covalent drug candidates.
Continued development and benchmarking of AI methods are critical for future breakthroughs in covalent drug design.