BioTech IP Review

Using computational models to:

• Generate hypotheses
• Rank candidates
• Eliminate low-probability experiments

before committing time, reagents, animals, or patients.

Key shift:

From experiment → insight
To simulation → experiment → validation

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Wet-lab steps most impacted

1. Target identification & validation

Before:

• Broad omics → wet validation loops

Now:

• Network biology
• AI target ranking
• Causal inference models

→ Fewer targets enter the lab.

2. Hit discovery

• Structure-based docking
• Ligand-based similarity
• Generative chemistry models

Replaces:

• Large random screens
• Many primary HTS campaigns

3. Lead optimization

• In silico ADMET
• Off-target prediction
• Binding affinity estimation

Cuts down:

• Iterative synthesis–test cycles

4. Biologic design

• Protein structure prediction
• Antibody affinity maturation
• Guide RNA design (CRISPR)

Wet lab becomes confirmatory, not exploratory.

5. Toxicity & safety

• In silico tox prediction
• Cardiac, liver, immune risk models

Doesn’t replace tox studies—but filters failures early.

Wet-lab activity	In silico replacement
Broad screening	Candidate prioritization
Trial-and-error	Model-guided design
Many failures	Early computational rejection
Manual intuition	Data-driven ranking

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Why this works now (not before)

1. Protein structure prediction
   • AlphaFold-class models
2. Large biological datasets
3. Cheap compute
4. Better error modeling
5. Closed-loop learning (lab ↔ model)

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Limits (important for credibility)

• Models reflect training data bias
• Biology has context AI can’t infer
• Rare toxicities are missed
• Wet lab remains the arbiter

In silico screening reduces search space, not biological uncertainty.

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Summary

Dimension	Traditional	In silico–first
Cost	High	Lower
Speed	Slow	Fast
Failure timing	Late	Early
Scale	Limited	Massive
Wet lab role	Discovery	Validation

In silico screening doesn’t replace experiments—it replaces bad experiments.