Customer Stories
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Accelerating Drug Discovery By AI-Driven Molecule Generation

Pharma & Biotech

Accelerating Drug Discovery By AI-Driven Molecule Generation

AI molecule generation platform to speed up early-stage drug discovery in skin rejuvenation.

12x

Less drug candidates in HTS

Molecules confirmed active through wet lab validation

10x

Faster preclinical narrowing compared to baseline

Customer Stories

For a series B biotech company working on skin rejuvenation therapies.

Pharma & Biotech

Industry

USA

Location

Generative AI modeling, transcriptomic analysis, pathway prediction, model validation

Services

$100,000–$200,000

Budget

Challenge

Identifying and prioritizing novel small molecules for skin rejuvenation using transcriptomic data and pathway analysis — in a scalable, efficient way to support early-phase drug development.

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Outcomes We Deliver

Solution

Blackthorn AI delivered a pipeline that combined generative AI, pathway graphs, and omics data ingestion to power molecule generation. The system enabled fast iteration, integration of biological constraints, and reduced reliance on trial-and-error lab synthesis.

Let’s talk about what’s possible

Tech Stack

To accelerate molecule discovery, Blackthorn AI used:

Python

PyTorch

Docker

Python

PyTorch

Docker

Roadmap

Project duration

1–2 Months

Initial Model & Analysis

Developed baseline predictive models from transcriptomics data and performed differential gene expression analysis.

3–4 Months

Pathway Mapping & Feature Design

Integrated pathway-level biological data to prioritize targets and refine compound features.

5–6 Months

Data Expansion & Model Training

Ingested additional RNASeq datasets and trained PerturbNet for more accurate compound activity prediction.

7–8 Months

Molecule Generation & Filtering

Generated thousands of novel compound candidates with skin rejuvenation potential using LLM-based tools.

9–10 Months

Handoff & Reporting

Finalized delivery, transferred documentation and results, and prepared for wet-lab validation phase.

1–2 Months

Initial Model & Analysis

Developed baseline predictive models from transcriptomics data and performed differential gene expression analysis.

3–4 Months

Pathway Mapping & Feature Design

Integrated pathway-level biological data to prioritize targets and refine compound features.

5–6 Months

Data Expansion & Model Training

Ingested additional RNASeq datasets and trained PerturbNet for more accurate compound activity prediction.

7–8 Months

Molecule Generation & Filtering

Generated thousands of novel compound candidates with skin rejuvenation potential using LLM-based tools.

9–10 Months

Handoff & Reporting

Finalized delivery, transferred documentation and results, and prepared for wet-lab validation phase.

Team Size

1 x Senior Bioinformatician

1 x AI Research Engineer

2 team members from Blackthorn.ai

1 x Senior Bioinformatician

1 x AI Research Engineer

Delivering Impact

Beyond the values already highlighted, there’s even more to discover. Our commitment to innovation, client success, and impactful results sets us apart.

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37

Molecules

Confirmed active compounds in wet lab – significantly validating model accuracy and ROI.

9

Months

Saved in candidate discovery

12x

Less

Drug candidates in wet lab HTS

37

Molecules

Confirmed active compounds in wet lab – significantly validating model accuracy and ROI.

9

Months

Saved in candidate discovery

12x

Less

Drug candidates in wet lab HTS

Accelerating Drug Discovery By AI-Driven Molecule Generation

For a series B biotech company working on skin rejuvenation therapies.

Pharma & Biotech

USA

Generative AI modeling, transcriptomic analysis, pathway prediction, model validation

$100,000–$200,000

Identifying and prioritizing novel small molecules for skin rejuvenation using transcriptomic data and pathway analysis — in a scalable, efficient way to support early-phase drug development.

Solution

To accelerate molecule discovery, Blackthorn AI used:

Project duration

1–2 Months

3–4 Months

5–6 Months

7–8 Months

9–10 Months

1–2 Months

3–4 Months

5–6 Months

7–8 Months

9–10 Months

Team Size

Delivering Impact

37

9

12x

37

9

12x

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