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High-Throughput Screening & Lab Automation

Build and operate high-throughput screening campaigns: assay miniaturisation, liquid-handling robotics, plate-based informatics, hit triage and Z-factor / QC analytics.

HTSOpentronsTecanSiLA2
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Default learning track

4 phases · ~16–22 weeks

A baseline path through this sector with milestones, prerequisites, and concrete projects. Click Personalize this roadmap above to have the AI tailor pace, depth, and resources to your background and goals.

1

Phase 1 — Foundations

3–4 weeks 30–40 hours

Build the conceptual and quantitative base needed to read papers and follow tutorials in the sector.

Prerequisites

  • Comfortable with basic biology / health-science vocabulary
  • Ability to install software and use a terminal

Skills you'll build

Python or R basicsLinux shellGit/GitHubReading scientific literature

Hands-on projects

Reproducible analysis notebook

Walk through a published tutorial in Opentrons / PyHamilton + Python plate analytics and reproduce its results on the provided sample data.

Deliverable: GitHub repo with a Jupyter/Quarto notebook, environment.yml, and README

Milestones

  • Set up reproducible Conda/Mamba environment and a public GitHub repo
  • Read and summarize 3 review papers covering the sector landscape
  • Complete an intro statistics or scripting course end-to-end
Citation · course
Coursera · Drug Development Product Management
UC San Diego
Why: Category match (Pharma & Biotech R&D)
2

Phase 2 — Core tools & datasets

4–6 weeks 60–80 hours

Learn the standard analytical stack of the sector and the canonical public datasets used by professionals.

Prerequisites

  • Completed Phase 1 reproducible notebook
  • Working Python/R environment

Skills you'll build

Opentrons / PyHamilton + Python plate analyticsData wranglingQC & exploratory analysisVersion-controlled pipelines

Hands-on projects

Dataset deep-dive on PubChem BioAssay + ChEMBL HTS datasets

Pick one study from PubChem BioAssay + ChEMBL HTS datasets, reproduce the headline result, and write a short technical note on what you found.

Deliverable: GitHub repo + 3-page PDF write-up

Milestones

  • Run the official Opentrons / PyHamilton + Python plate analytics tutorial end-to-end on real data
  • Download and explore one full dataset from PubChem BioAssay + ChEMBL HTS datasets
  • Document a clean QC + analysis pipeline that another person could rerun
Citation · training
FDA Learn portal
U.S. FDA
Why: Category match (Pharma & Biotech R&D)
3

Phase 3 — Applied projects

6–8 weeks 80–120 hours

Move from tutorials to original analyses on real questions. Start showing your work publicly.

Prerequisites

  • Phase 2 dataset deep-dive complete
  • Comfortable with the sector's primary tooling

Skills you'll build

End-to-end pipeline designStatistical interpretationScientific writingReproducible reports

Hands-on projects

HTS mini-campaign simulation

Design a 384-well screen for a chosen target: pick assay format, simulate plate data with controls, compute Z′ and triage hits.

Deliverable: Assay protocol + Python notebook + hit list with QC plots

Milestones

  • Ship one original mini-analysis on a public dataset with clearly stated hypothesis and limitations
  • Engage with the SLAS lab-automation community (post a question, answer one, or share a notebook)
  • Get peer feedback on at least one project and iterate
Citation · documentation
ICH Guidelines
ICH
Why: Category match (Pharma & Biotech R&D)
4

Phase 4 — Portfolio & career launch

3–5 weeks 30–50 hours

Package your work, target real roles, and prepare to interview in the sector.

Prerequisites

  • Phase 3 capstone project shipped
  • 2+ public repos under version control

Skills you'll build

Technical CVPortfolio siteInterview prep (case studies + technical questions)Networking

Hands-on projects

Job-ready portfolio package

Curate 2–3 of your strongest sector projects into a portfolio site with clear case-study writeups, plus a 1-page CV tailored to the target role.

Deliverable: Live portfolio URL + PDF CV + cover letter template

Milestones

  • Publish a portfolio site or pinned GitHub README linking to 2–3 projects
  • Tailor CV to 3 real job ads in the sector and submit applications
  • Practice 5 mock technical interviews with sector-specific case studies
Citation · training
Benchling Academy
Benchling
Why: Category match (Pharma & Biotech R&D)

Curated learning resources

Verified, canonical resources from the official providers in this sector. The AI roadmap builder draws from this same library when it personalizes your roadmap.

Courses

Coursera · Drug Development Product Management
UC San Diego
Python for Everybody
Coursera · Univ. of Michigan
LinkedIn Learning paths
LinkedIn

Trainings

FDA Learn portal
U.S. FDA
Benchling Academy
Benchling
Software Carpentry lessons
The Carpentries
Kaggle Learn
Kaggle

Documentations

ICH Guidelines
ICH
Git & GitHub fundamentals
GitHub Docs

Books

R for Data Science (2e)
Hadley Wickham et al.

Communitys

AAPS membership & resources
AAPS

Typical career paths

Automation Scientist

Programs liquid handlers and integrated workcells to run HTS campaigns.

HTS Data Scientist

Builds plate analytics, hit-calling and dose-response pipelines.

Ready to make this your own?

Answer a short profile and the AI builder will tailor every phase — pace, hours, tools, and resources — to your background and goals in this sector.

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