Research Operations
The Hidden Cost of the Wrong Wet-Lab Decision
It's not just the direct bench cost. It's the compounding opportunity cost of scientist-months spent on a target that was never going to validate.
Assaygrove
Research & Thinking
Notes on AI target discovery, multi-omics analysis, and the decisions that shape early-stage drug programs.
Target Discovery
Why Your Drug Target List Is Probably Wrong
The standard workflow for generating a target shortlist is broken in ways most research teams don't notice until they're deep into a failed program.
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Omics Analysis
Proteomics vs Genomics for Target Evidence: Which to Trust More?
When genomics and proteomics data point in different directions, the answer isn't always to trust the GWAS hit.
Target Discovery
When AI Gets Target Discovery Wrong (and What to Do About It)
We've seen several ways AI target ranking fails. Most come back to the same root issue: garbage-in garbage-out dressed in a confidence score.
Omics Analysis
Multi-Omics Integration: How Transcriptomics and Phenomics Together Sharpen a Target Hypothesis
Neither transcriptomics nor phenomics alone tells the full story. The value is in how they constrain each other.
Research Operations
Building a Reproducible Target Assessment Workflow
The analysis you can re-run six months later with the same results is the analysis you can defend in a go/no-go meeting.
Target Discovery
What Does a Target Confidence Score Actually Mean?
Confidence scores are only as useful as the evidence hierarchy that generates them. Here's how to interpret ours.
Research Operations
Bench Budget and Wet-Lab Capacity: How to Think About Prioritization Tradeoffs
The right target for a $5M runway team looks different from the right target for a 40-person group. Budget constraints are evidence too.
Literature Mining
Literature Mining for Target Discovery: Automation vs Manual Review
A manual PubMed review catches nuance an automated system misses. But the automated system reads 40 million records before lunch.
Target Discovery
Evidence Hierarchies in Target Prioritization: Genetic First, Always?
The drug discovery community has near-consensus: genetic evidence is the strongest prior for target validity. We agree, with one important caveat.
Omics Analysis
Protein Interaction Networks and the Problem of Study Bias
High-throughput protein interaction data is extremely useful for target scoring, and extremely biased toward proteins the community has studied most.
Omics Analysis
Omics Data Quality and Preprocessing: What Gets Missed Before Analysis
Most downstream analysis errors are locked in before the model sees the first row of data. The preprocessing step is where target discovery quietly fails.
Target Discovery
What Computational Biologists Know That Your Target List Doesn't
An experienced computational biologist spends months on a target ID problem, cross-referencing signals, weighting evidence, following up contradictions.