Case Studies

Real programs. Real validation data.

Client identities are anonymized at their request, but every metric below is from a closed engagement with downstream wet-lab confirmation.

80–90%: Average cost reduction
3–6 mo: Average time savings
87%: Wet-lab prediction accuracy
100%: On-time delivery rate

Case 01

Oncology Biotech

EGFR inhibitors for resistant tumors

90%: Cost savings vs. prior campaign
3.5x: Faster time-to-lead
8: Active compounds confirmed
2: Advanced to preclinical testing

Challenge

A clinical-stage oncology team needed novel EGFR inhibitors active against T790M-resistant tumors. Their internal HTS pipeline had screened 6,000 compounds over 8 months at a cost of $1.8M with limited progress.

Approach

We screened 2.5M AI-ranked candidates in silico, applied docking + MD refinement against the resistant binding pocket, and delivered 75 top picks for wet-lab confirmation.

AI predictions were 87 percent accurate vs. wet-lab validation, far above the 30 to 40 percent typical for off-the-shelf docking.

Case 02

Gene Therapy Startup

Codon-optimized CRISPR guide design

95%+: On-target specificity
0: Detected off-target cuts
10 days: End-to-end delivery
5: Top sequences validated

Challenge

A Series A gene therapy startup needed CRISPR guide sequences for a liver-targeted therapeutic. They had no in-house computational team and were quoted a 2-month timeline by a CRO.

Approach

Generated 500K sequence variants, predicted off-target effects across the human genome, and ranked by on-target activity and manufacturability.

All five top-ranked sequences passed in vitro validation on the first pass. No second iteration needed.

Your program could be the next case study.

Tell us about your target. We'll scope a 2–4 week pilot.

Start a pilot