AI-driven drug discovery — Computational acceleration from target to lead

2026年01月07日 15:10

AI-driven drug discovery accelerates hypothesis generation and compound prioritization by coupling data curation with computational learning and physics-informed evaluation. Key applications include: 1、Structure- and ligand-based virtual screening to rank candidates and propose binding hypotheses 2、Molecular design and optimization guided by predictive models for potency and developability 3、ADMET risk forecasting integrated with docking and molecular dynamics to enable iterative, evidence-based refinement The white paper introduces the end-to-end framework, while the implementation guide clarifies workflows, outputs, and iteration loops.

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Chemical proteomics----implementation guide

2026-01-07 15:10:47.358

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AI-driven drug discovery accelerates hypothesis generation and compound prioritization by coupling data curation with computational learning and physics-informed evaluation. Key applications include:
1、Structure- and ligand-based virtual screening to rank candidates and propose binding hypotheses
2、Molecular design and optimization guided by predictive models for potency and developability
3、ADMET risk forecasting integrated with docking and molecular dynamics to enable iterative, evidence-based refinement
The white paper introduces the end-to-end framework, while the implementation guide clarifies workflows, outputs, and iteration loops.

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Chemical Proteomics
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Targeted Degradation
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