An integrated AIDD workflow spanning target understanding, hit discovery, lead optimization, and experiment-guided preclinical decisions.
An integrated AIDD pipeline combines target interpretation, computational screening, lead refinement, and iterative dry-lab / wet-lab feedback within a single modular discovery framework.
A stage-based map of how AIDD modules can move from target understanding to decision-ready candidate packages.
Build a computation-ready view of the biological system through structure prediction, pocket analysis, and conformational assessment.
Use structure-based virtual screening and AI-assisted search to reduce large chemical spaces into focused, testable candidate sets.
Improve potency, selectivity, and developability through simulation-guided design, free-energy analysis, and property prediction.
Connect computational outputs with medicinal chemistry, assay results, and candidate triage to support downstream experimental programs.
The workflow can be read as stacked module layers: target intelligence, molecule design, and optimization-to-translation.
The front end of the workflow builds mechanistic context before large-scale molecule ranking begins.
Search, docking, ranking, and generation modules reduce the candidate space and expand design options.
Physics-based and data-driven modules support refinement, developability review, and downstream handoff.
The pipeline is not linear. Computational prioritization and experimental readout keep updating each other.
Experimental feedback updates the ranking logic, simulation priorities, and design hypotheses instead of ending the computational workflow after a single pass.
AIDD integrates structure-based modeling, machine-learning-assisted prioritization, and physics-based simulation to support more informed decisions across the drug discovery process. Rather than treating computation as a single screening step, the workflow connects target understanding, molecule generation, hit triage, and lead refinement as parts of the same discovery system.
The value of this approach lies in the continuous exchange between computation and experiment. Structural hypotheses can guide compound selection, assay readouts can refine ranking logic, and simulation can help interpret emerging SAR, allowing the workflow to remain adaptive throughout hit discovery and lead optimization.