For Bench Scientists — Pilot Cohort Q3 2026

The Fast
and the
Curious

AI Upskilling Built for Bench Scientists

An 8-week, hands-on program that is taught in-person in Cambridge, Massachusetts, transforming a cohort of driven bench scientists into confident AI practitioners — no CS degree required. Predict protein structures, design molecules with generative AI, and learn how to use a full spectrum of AI-powered drug development tools for the next generation of drug development.

8Weeks
~5 hrsPer week
$2,500Full program
20Max cohort
Find out how to take this course at NO COST TO YOU →
Brought to you by Scriptome.AI
Merelogic
Pilot cohort capped at 15–20 scientists

Reserve Your Spot

Join the waitlist for our first cohort — launching Q3 2026.

The Problem

AI is reshaping biotech.
Most bench scientists are getting left behind.

The tools are here — AlphaFold, Boltz, generative molecular design, LLM-powered literature mining. They’re already in leading labs. But no one is teaching bench scientists how to use them — and the skills gap is growing fast.

+35%
Computing & IT growth in MA biotech

The fastest-growing segment in life sciences

Demand for AI-fluent scientists is outpacing supply across every therapeutic area. Companies are hiring for skills most bench scientists have never been taught.

4–5×
Reskilling demand vs. new hiring

The math doesn’t work without training existing talent

2,400+ MA bench scientists need reskilling. Only 512 new hires can fill that gap. That means you need these skills — and you need them now.

$15K+
Average cost of existing AI programs

Built for the wrong audience

Most courses take 6–12 months, assume a CS background, and cost a small fortune. That doesn’t work for someone running experiments full-time.

What You’ll Learn

Eight skills. Eight weeks.
Zero CS background required.

Every module is built around real drug R&D workflows. You leave with working skills — not just a certificate.

Weeks 1–2

Protein Structure & Binding Prediction

Use Boltz and OpenFold hands-on, building your competency in leveraging these powerful drug development tools.

Week 3

Generative Molecular Design

Design novel drug-like compounds with generative AI tools. Understand how they fit your discovery pipeline from day one.

Week 4

ADMET Prediction & Drug-Likeness

Evaluate compound viability early using AI to predict absorption, distribution, metabolism, excretion, and toxicity.

Week 5

LLM-Powered Literature Mining

Find research gaps 10x faster than manual search. Synthesize hundreds of papers in the time it takes to read ten.

Week 6

Data Analysis & Visualization

Turn raw experimental data into insights using low-code Python notebooks — copy, paste, and modify. No from-scratch coding.

Weeks 7–8

Target ID and Virtual Cells

Learn how to identify and prioritize promising drug targets, then explore virtual cell models that simulate biology to predict experimental outcomes before you run them.

Full Curriculum

Week-by-week breakdown

No-code to low-code progression. Each week has a concrete deliverable — you build a portfolio as you go.

Phase 1 · Weeks 1–3 No-code tools — web interfaces & Google Colab
Week
Topic
Format
01
AI Foundations for Drug Discovery
In-person workshop
02
Protein Structure & Binding Prediction (Boltz & OpenFold)
Hands-on lab session
03
Molecular Design with Generative AI
Hands-on lab session
Phase 2 · Weeks 4–6 Low-code Python notebooks — copy / paste / modify
Week
Topic
Format
04
ADMET Prediction & Drug-Likeness
Hands-on lab session
05
Literature Mining with LLMs
Hands-on lab session
06
Data Analysis & Visualization
In-person workshop
Phase 3 · Weeks 7–8 Target ID & Working with Virtual Cells
Week
Topic
Format
07
Target Identification
Workshop + case studies
08
Working with Virtual Cells
In-person workshop
Why It Works for Bench Scientists

Built for bench scientists learning AI —
not data scientists learning biology.

🎯

Problem-First, Not Theory-First

We start with “how do I predict this protein structure?” not “what is a neural network?” Theory only when you need it to solve a real problem.

📈

No-Code to Low-Code Progression

Weeks 1–3: web interfaces only. Weeks 4–6: Python notebooks (copy/paste). Weeks 7–8: customize. Confidence builds naturally.

🔬

Skills You Take With You

Every technique is taught on curated, non-proprietary drug discovery datasets — so you build real competencies without ever touching sensitive company data. What you learn transfers directly to your own team’s problems on day one back at work.

🛡

Vendor-Agnostic & Open-Source

We teach critical evaluation, not platform dependence. No lock-in, no upsells. Everything runs on free-tier tools including Google Colab.

🏭

Industry Guest Instructors

Real data scientists and AI leads from MA biotechs join as guest instructors. Hear what companies actually want — from the people doing the hiring.

⚡

50%+ Hands-On Time

Every concept is demonstrated with real drug discovery tools. Fail-forward culture. Real datasets, real problems.

How We Compare

83% less than Stanford.
Twice the hands-on time.

ProgramDurationCostHands-On?Biotech-Specific?
MIT AI in Pharma6 weeks$2,800LimitedYes
Stanford AI Certificate6–12 months$15,000+ModerateNo
Coursera BioinformaticsSelf-paced$49–79/moLimitedPartial
▶ The Fast & The Curious8 weeks$2,500Extensive (50%+)100% Biotech
Pricing

Priced for real scientists,
not corporate training budgets.

Transparent pricing, no hidden fees.

Individual

$2,500

per participant · full 8-week program

Complete curriculum, hands-on sessions, industry panel, and certificate of completion.

  • 8 weeks of live instruction
  • All tools & materials included
  • Certificate of completion
  • Capstone portfolio project
  • Slack community access
Join Waitlist
Best Value

Corporate Team

$10,000

for 5 employees · 20% team discount

Upskill your team together. Shared cohort experience builds internal AI culture at your company.

  • Everything in Individual
  • 20% savings vs. individual rate
  • Dedicated team office hours
  • Manager progress reporting
  • Priority access to future cohorts
Join Waitlist

Need-Based Scholarship

Free

3 full scholarships per cohort

Funded by industry partners. Cost should never be a barrier for scientists who need these skills most.

  • Full program access
  • Industry-sponsored
  • Application-based selection
  • Same curriculum & support
Apply for Scholarship
Your Instructors

Taught by practitioners.
Not academics.

Both instructors have spent years in the field — in labs, in startups, and building AI tools for real drug discovery.

Stu Angus

Stu Angus

Founder & Lead Instructor

Scriptome.AI · Cambridge, MA

15+ years in drug development at Sanofi Genzyme and Hopewell Therapeutics. MBA in Business Analytics (Isenberg). Host of the Scriptome Podcast and speaker at MIT’s Internet of AI Agents Conference.

Jesse Johnson

Jesse Johnson

Co-Instructor & Co-Promoter

Merelogic

Jesse brings deep expertise in computational biology and practical AI applications across the drug discovery pipeline — adding industry depth and a complementary perspective on AI tools for life sciences professionals.

“The talent supply-demand imbalance persists — especially in AI integration, technology, and machine learning. A clear need for the industry.”

— Massachusetts Life Sciences Executive, MassBioEd Report 2025

Pilot Cohort · Q3 2026

Ready to become an
AI-fluent bench scientist?

Join the waitlist. Get first access to enrollment and early-bird pricing.

Pilot cohort capped at 20 scientists