Complete End-to-End Knowledge Framework for Computational Drug Development

 

Most CADD conversations still get stuck at “we do docking and virtual screening.” That’s the bare minimum.

I’ve been working on a Complete End-to-End Knowledge Framework for Computational Drug Development that treats CADD as a lifespan function – from target ideation to Phase III support and regulatory expectations.
Here’s how I’m framing it:

Stage 1 – Target ID & Validation: Human genetics first, not last. Multi‑omics, Mendelian randomisation, tractability, and druggability are assessed in parallel, not sequentially.

Stage 2 – Hit Discovery: Structure‑ and ligand‑based VS, docking rigor (self‑docking, protonation sanity checks, consensus scoring), and clear enrichment strategies before we waste a single HTS plate.
Stage 3 – Hit‑to‑Lead: Systematic triage with ligand efficiency metrics, PAINS/aggregator cleanup, selectivity, and early ADMET filters baked into entry criteria.

Stage 4 – Lead Optimisation: True MPO (not potency worship), FEP/MD for decision‑grade ΔΔG, and consensus in silico ADMET instead of single‑model wishful thinking.

Stage 5 – Candidate Selection: Explicit computational gates for activity, ADMET, PK, physicochemical space, and translational readiness.

Stage 6 – Clinical Support: PBPK, exposure–response, biomarker strategy, and patient‑selection biomarkers feeding back into development, not treated as “nice to have.”

Stage 7 – Cross‑cutting: Data curation, reproducibility, regulatory awareness (ICH M7, PBPK guidance), and disciplined communication so predictions are audit‑ready and decision‑useful.

This framework is Version 1.0, built for CADD training, project planning, and interview prep in an industrial setting. My goal is simple: move the conversation from “we ran a docking campaign” to “we run an evidence‑driven, auditable, end‑to‑end computational strategy that actually de‑risks programs.”

If you’re a CADD / medchem / translational scientist (or building a computational function from scratch), I’m happy to share the framework and walk through how I’d adapt it for:

Early‑stage biotech with limited experimental bandwidth
Big‑pharma environments with heavy legacy data and regulatory scrutiny
Academic–industry collaborations where reproducibility and communication usually break first.

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