AI in Drug Discovery and Development Corporate Training
Build AI-assisted discovery pipelines, from molecular modeling to clinical analytics, in guided hands-on labs.
This instructor-led program takes your team from AI fundamentals to a working discovery pipeline. Through guided labs, your scientists and data teams practice molecular representation, predictive ADMET modeling, generative design, virtual screening, and clinical data analytics, all within compliant, production-style workflows. The program closes with a reviewed capstone: an end-to-end AI-assisted discovery pipeline your team designs, builds, and defends.
Instructor-led group trainingVirtual / on-site / off-site10 languagesDelivered inEnglish, Spanish, Mandarin, German, Arabic, Portuguese, Hindi, French, Japanese, Italian100+ countriesWe have delivered inUnited States, United Kingdom, India, Canada, Germany, France, Netherlands, UAE, Saudi Arabia, Singapore, Australia, Japan, Brazil, South Africa and 90+ more countries
How we build an AI-ready discovery team
The Edstellar AI DRUG DISCOVERY Capability Model
Assess discovery skills
Skill checks map your team's data fluency, modeling basics, and compliance awareness.
Set shared standards
One playbook for data curation, model validation, and documentation across projects.
Practice in guided labs
Labs cover cheminformatics, predictive modeling, and generative design workflows.
Measure the lift
Lab reviews benchmark each scientist's applied AI modeling capability.
Scale across teams
One standard rolls out across therapeutic areas, sites, and teams.
Need more than one program?Get a group quote
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Program Overview: What is AI in Drug Discovery and Development?
AI in Drug Discovery & Development is Edstellar's instructor-led technical program for medicinal chemists, computational scientists, and clinical researchers applying AI across the discovery pipeline. Participants learn AI foundations, molecular data and cheminformatics, predictive QSAR and ADMET modeling, generative design, and clinical data compliance. The program connects molecular data to validated discovery decisions.
Training is hands-on from the first module. Participants curate molecular datasets, engineer descriptors, and build QSAR models in guided labs built around realistic discovery workflows. Instructors coach them through virtual screening, generative molecule design, and hit prioritization, reviewing every exercise with direct feedback. The program closes with a capstone where each team builds an end-to-end discovery pipeline from data to shortlist.
Discovery teams lose months to slow screening, unreliable predictions, and models that fail validation. This program teaches scientists to run AI inside the workflows they already trust, with validation and GxP checks that keep every output defensible. Every technique is practiced in labs, so the learning transfers to the next pipeline.
Scientists design an end-to-end discovery pipeline and defend a candidate shortlist under instructor review. The models are only as good as the validation, and the validation happens in the room.
The program is built around guided, hands-on labs that mirror real discovery workflows: curating molecular datasets, engineering descriptors, training and validating predictive models, running generative design and docking studies, and analyzing clinical and real-world data. Every technique is practiced in a compliant, production-style environment, so your scientists leave with workflows they can apply to live projects immediately, not just theory.
What's Included in this Workshop for Your Enterprise Teams
- 1
Molecular data labs
Curate and featurize real compound datasets.
- 2
Predictive modeling
Train and validate QSAR and ADMET models.
- 3
Generative design
Propose and prioritize novel candidates.
- 4
Clinical analytics
Analyze trial and real-world data compliantly.
- 5
Capstone pipeline
Build and defend an end-to-end discovery pipeline.
- 24 instructor-led hours scheduled around your team
- Live virtual, onsite, or hybrid delivery
- Guided hands-on labs in every module
- Reviewed capstone pipeline build
- Lab guides and workflow templates
- Skills assessment with the Skills Matrix
Build your team's skills. Anywhere in the world.
Real-world use cases
from day one
Instructor-led training
for your teams
Delivered globally
in 100+ countries
Enterprise Training Solutions Customized for Your Company
One partner for instructor-led corporate training, delivered the way your teams work.
Request a Training Quote- Customized Training Delivery
- Scale Your Training: Small to Large Teams
- In-person Onsite, Live Virtual or Hybrid Training Modes
- Plan from 2000+ Industry-ready Training Programs
- Hands-On Labs on Live Infrastructure, Led by Industry Experts
AI in Drug Discovery and Development Course Topics and Program Outline
Six modules move from AI fundamentals to a reviewed capstone pipeline, with guided labs in every module and compliance woven throughout.
Built by Edstellar with drug discovery AI practitioners
Built by practitioners who apply AI to discovery pipelines in production, shaped around your stack and use cases.
Skill checks baseline your team's data fluency, modeling readiness, and compliance awareness before training begins.
Instructor-led sessions build the AI, cheminformatics, and regulatory foundations your discovery work depends on.
Guided labs walk your team through modeling, screening, and design workflows on realistic compound datasets.
Your team integrates every technique into a reviewed capstone pipeline, ready to adapt to live projects.
From molecular data basics to a capstone discovery pipeline, every technique is practiced in guided labs.
Delivered as
Delivered as a hybrid program of live instruction and guided labs, scheduled around your team's calendar in flexible batches.
Explore curated compound datasets and identify quality issues in a guided data audit.
learna)AI and ML landscape across the discovery pipeline
b)Molecular, genomic, and clinical data types
c)Data quality, bias, and curation principles
d)Regulatory context: 21 CFR Part 11 and data integrity
Build a curated, featurized dataset from raw compound records.
practicea)SMILES, fingerprints, and molecular graphs
b)Descriptor and feature engineering
c)Dataset curation and splitting strategies
d)Cheminformatics tooling in guided labs
Train, validate, and interpret an ADMET prediction model.
practicea)QSAR modeling workflows
b)ADMET property prediction
c)Validation: cross-validation and applicability domain
d)Interpreting models for chemistry decisions
Generate candidates and rank them through a screening workflow.
practicea)Generative models for molecule design
b)Virtual screening and docking workflows
c)Hit prioritization and triage
d)Design-make-test-analyze loop integration
Analyze a trial dataset and document the workflow for audit.
applya)Trial design optimization with AI
b)Real-world data and evidence generation
c)GxP, validation, and documentation practices
d)Handoff: from discovery to development
Build and defend a complete AI-assisted discovery pipeline.
applya)Pipeline architecture and tooling choices
b)End-to-end build: data to shortlist
c)Validation, documentation, and compliance checks
d)Review: defend your pipeline and results
Customize this program tailored to your team?
Tell us your training needs: what your team already works with, the level they’re at, and the topics you want them to focus on. We’ll adjust the modules, duration and hands-on labs, and send you a customized outline for your group.
Key Skills and Outcomes Your Team Will Gain
By the end of this program, your team will model, screen, and document AI-assisted discovery workflows with confidence.
Map AI use cases across discovery
Identify where machine learning adds value from target identification through lead optimization.
Curate molecular datasets
Clean, validate, and prepare compound data so models train on trustworthy inputs.
Engineer molecular features
Build fingerprints and descriptors that capture the chemistry models need.
Train QSAR and ADMET models
Build supervised models that predict activity and safety properties reliably.
Run generative design workflows
Propose novel candidates with generative methods and prioritize them for testing.
Screen virtually with confidence
Apply docking and triage workflows to focus experimental work on the best bets.
Document for compliance
Keep AI workflows audit-ready with validation records and GxP-aligned documentation.
Teams that Benefit from this AI in Drug Discovery and Development Group Training
This program serves medicinal chemists, computational scientists, and clinical development teams bringing AI into discovery workflows. Content adapts to each cohort's role and experience, so every group practices the modeling and analytics workflows it owns, from data curation to candidate selection.
Give discovery scientists practical AI modeling skills.
Medicinal chemists and discovery scientists learn to curate data, train predictive models, and use generative design in daily research, with labs built on realistic compound datasets.
Workflow friction
- Hit rates stall without predictive prioritization.
- Compound data stays messy and underused.
- AI pilots never reach daily workflows.
How the program helps
- Prioritize compounds with validated models.
- Practice on realistic discovery datasets.
- Bring AI into everyday chemistry decisions.
Practice use cases
- QSAR modeling
- ADMET prediction
- Hit triage
- Generative design
Standardize modeling workflows across projects.
Computational chemists and modelers build rigorous, validated pipelines for screening and design, with shared standards for validation, documentation, and handoff to experimental teams.
Workflow friction
- Models lack rigorous validation standards.
- Screening workflows vary by project.
- Handoffs to wet labs lose context.
How the program helps
- Apply consistent validation practices.
- Standardize screening and design workflows.
- Document models for reliable handoffs.
Practice use cases
- Virtual screening
- Docking studies
- Model validation
- Pipeline design
Connect discovery AI to trial analytics.
Clinical operations and development teams learn how AI-informed discovery decisions flow into trial design, and practice analyzing clinical and real-world data with compliant, auditable methods.
Workflow friction
- Discovery and clinical teams work in silos.
- Trial data analysis stays manual.
- Compliance slows analytics adoption.
How the program helps
- Link discovery outputs to trial design.
- Analyze clinical data with AI assistance.
- Keep analytics audit-ready from day one.
Practice use cases
- Trial optimization
- Real-world data
- Safety analytics
Keep AI workflows inspection-ready.
Regulatory and quality professionals learn what good AI documentation looks like: validation records, data integrity controls, and GxP-aligned practices that stand up to inspection.
Workflow friction
- AI documentation standards are unclear.
- Data integrity gaps surface at inspection.
- Validation expectations keep shifting.
How the program helps
- Define audit-ready AI documentation.
- Apply data integrity controls in labs.
- Align teams on validation expectations.
Practice use cases
- Validation records
- Data integrity
- GxP alignment
Each department track uses the same validated pipeline framework, tuned to the datasets and decisions that team owns.
Prerequisites: This program benefits medicinal chemists, computational scientists, and clinical development professionals most. Familiarity with basic chemistry or biology concepts helps, but no prior AI or machine learning experience is required. Content adapts to each cohort's tools and therapeutic focus, so teams practice on workflows and datasets close to their real projects.
Research Associate to Discovery Lead
Early-career scientists build data and modeling fundamentals; experienced researchers advance to pipeline design and review.
One program that grows with discovery careers, from data basics to confident pipeline ownership.
Mixed cohort, or several teams at once?
We can tailor the training to different teams, roles, and experience levels within the same program.
Certificate of Training Completion.
On successful completion, employees receive an Edstellar course completion certificate, recognizing their commitment to ongoing learning and professional development. It validates the skills they have built and motivates them to keep growing and contribute to organizational success.
Earned, not attended. Awarded on completion of all hands-on labs and the capstone project.
Training Delivery Modes: Virtual, Onsite and Hybrid
Choose the format that fits your teams and lab needs. Every mode includes live instruction, guided labs, and instructor feedback on the capstone.
Hybrid Training
Blend live virtual sessions with onsite workshop days so distributed teams learn together. Instructors guide every lab in real time across both settings.
- Live instruction across virtual and onsite
- Guided labs with real-time instructor support
- Flexible scheduling around team calendars
- Capstone review in your preferred format
Onsite Training
Bring the instructor to your site for focused, team-based sessions. Onsite delivery suits teams that want whiteboard collaboration and immediate feedback during intensive lab days.
- Instructor at your location
- Team-based lab collaboration
- Whiteboard design sessions
- Immediate feedback on models
Live Virtual Training
Join live instructor-led sessions from anywhere with full lab access. Small cohorts, real-time guidance, and shared screens make virtual delivery fully interactive.
- Live sessions across time zones
- Full lab access from anywhere
- Small cohorts, real-time guidance
- Shared screens for model reviews
Need a training format that works for your team?
Full days, half days across weeks, or split by module. Tell us the delivery window and we schedule the cohort around it.
Learn from industry experts.
Every session is led by a practitioner who has run monitoring for models in production, not a generalist working from slides.
Chandrika
Data Science and Machine Learning Trainer in Mumbai
10+ years of Experience
Sudhanshu
Machine Learning Trainer in Bareilly
19+ years of Experience
Anudeep
Data Science with Python Trainer in Hyderabad
11+ years of Experience
Deepesh
Generative AI Trainer in Gaziabad
8+ years of Experience
Priyadharshini
Machine Learning with Python Trainer in Chennai
4+ years of Experience
Udayraj
Deep Learning and Python Trainer in Ahmednagar
7+ years of Experience
Soudamini
Data Science Trainer in Hyderabad
13+ years of Experience
Trainers are matched to your stack and delivery window at scheduling. We confirm your assigned trainer, with a full profile, before the program is booked.
Frequently Asked Questions
Share your team size and preferred dates through the lead form, and we will send a tailored proposal with pricing and scheduling options.
Yes. Every engagement starts with a free discovery call so we can understand your discovery workflows and shape the program around your goals.
Yes. Live virtual delivery runs with full lab access, small cohorts, and real-time instructor guidance across time zones.
Yes. Onsite delivery brings the instructor to your office for focused, team-based sessions with whiteboard collaboration.
The program runs 24 instructor-led hours, typically delivered across several sessions scheduled around your team's calendar.
Question not answered here?
Put it in the request. A training specialist answers it directly, within one business day.
Where this Training Fits in Your Team's learning path.
AI in drug discovery builds on bioinformatics and genomic data skills, the data AI models learn from. After discovery, the path follows the pipeline into clinical trials and then into real-world evidence, so your team sees AI across the whole of development.
Why Choose Edstellar as Your AI in Drug Discovery and Development Corporate Training Provider
Edstellar is the training provider life sciences teams choose when AI discovery capability must improve fast. With 5,000+ certified trainers, we bring proven methodology, hands-on labs, and measurable outcomes to your discovery program.
EXPERT TRAINERS
YEARS DELIVERING
ORGANIZATIONS TRAINED
CERTIFIED
Tailored
Labs use your therapeutic focus and data types, so scientists practice on the discovery workflows they actually own.
Proven Methodology
A diagnose-to-scale path moves teams from baseline assessment to measured AI modeling capability, cohort by cohort.
Practice-Based Learning
Guided labs in every module let scientists build QSAR models, screening workflows, and pipelines with expert feedback.
Expert Trainers
Learn from practitioners who apply AI to live discovery pipelines and coach teams through real modeling trade-offs.
Global Scale
Consistent delivery across sites and time zones keeps every discovery team on the same validated standards.
Measurable Outcomes
Skills Matrix assessments and the reviewed capstone give you hard evidence of your team's modeling lift.
Also from Edstellar
Talent assessments
Psychometric, behavioral, leadership and 360-degree assessment.
→Training needs analysis
Needs analysis, gap identification, and a training roadmap.
→L&D consulting
Learning strategy, content, technology and ROI measurement.
→OD consulting
Org design, succession planning and cultural change.
→Managed training services
Training outsourcing, vendor management, logistics and administration.
→Coaching solutions
Executive and manager coaching that keeps skills in use.
Start an RFP shaped to your needs.
Answer three quick questions about team size, scope, and how often you need the program run, then send the request from the last step. Everything you choose travels with it, so you only fill this in once.
How many team members need training?
AI in Drug Discovery & Development Training Packages & Pricing
Where L&D holds an annual budget, license packages price the whole year at once instead of raising a purchase order per workshop. Hours and licenses are pooled across teams, programs and locations, so unused capacity is not stranded in one department.
Starter
120
trainee licenses64 hours of group training
Tailored for SMBs
Growth
320
trainee licenses160 hours of group training
Ideal for growing SMBs
Enterprise
800
trainee licenses400 hours of group training
Designed for large corporations
Custom
Unlimited
trainee licensesDuration set by the rollout
Multi-region and multi-year
Training Packages & Pricing
Choose the training format that works for your team. Pricing is customized based on your requirements, cohort size, and delivery needs.
Request AI in Drug Discovery and Development Training for Your Team
Request AI in Drug Discovery & Development training. Share your goals, team size, and schedule, or book a discovery call, and we will prepare a tailored plan for your discovery teams.