Overview of the Course
This professional-grade program is designed to provide mastery over Machine Learning for Drug Discovery, empowering scientists and engineers to revolutionize the Pharmaceutical R&D pipeline. Participants will explore the implementation of Deep Learning, Generative Chemistry, and Predictive Modeling to accelerate Lead Optimization, Virtual Screening, and ADMET Prediction. By mastering Bioinformatics, Cheminformatics, and Cloud Computing, learners will gain the skills necessary to build scalable Artificial Intelligence solutions that reduce drug development timelines and improve clinical success rates.
The course provides a deep dive into the integration of AI across the entire drug development lifecycle, from target identification to clinical trial simulation. You will learn to utilize advanced algorithms like Graph Neural Networks and Variational Autoencoders for de novo drug design while exploring specialized features like molecular docking and protein folding. The training concludes with a focus on MLOps for pharma, ensuring that models are validated, interpretable, and ready for regulatory scrutiny in a production environment.
Who should attend the training
- Medicinal Chemists and Pharmacologists
- Bioinformatics and Computational Biologists
- Data Scientists and ML Engineers in Life Sciences
- R&D Managers and Pharmaceutical Executives
- Clinical Research Associates and Trial Coordinators
- Academic Researchers in Biotechnology
Objectives of the training
- To understand the role of AI in revolutionizing traditional drug discovery workflows.
- To apply supervised learning for predicting molecular bioactivity and toxicity.
- To leverage generative models for de novo molecular design and synthesis.
- To utilize deep learning for automated medical imaging and digital pathology.
- To implement ethical and regulatory best practices for AI in clinical settings.
Personal benefits
- Attain a high level of proficiency in specialized AI applications for the biotech sector.
- Bridge the gap between classical chemistry and modern computational data science.
- Enhance your resume with validated skills in high-demand MLOps and Cheminformatics.
- Gain the ability to lead AI-driven innovation within a pharmaceutical research team.
Organizational benefits
- Drastically reduce the cost and time of early-stage drug candidate identification.
- Standardize data-driven workflows to improve collaboration between lab and computational teams.
- Accelerate time-to-market for novel therapeutics through efficient lead optimization.
- Ensure data governance and regulatory compliance within AI-assisted research processes.
Training methodology
- Instructor-led demonstrations of specialized AI platforms and Python libraries
- Hands-on laboratory exercises using curated chemical and biological datasets
- Real-world case study simulations for target validation and virtual screening
- Interactive problem-solving for molecular representation and feature engineering
- Collaborative peer review of model interpretability and validation results
Trainer Experience
Our trainers are seasoned experts with decades of combined experience in computational drug design and machine learning engineering. They have led AI initiatives at major global pharmaceutical firms and hold advanced degrees in Bioinformatics and AI, bringing deep practical knowledge of both the wet-lab and dry-lab requirements of the industry.
Quality Statement
We are committed to providing the most current and relevant technical training. Our course modules are updated in alignment with the latest advancements in Transformer architectures and Generative AI, ensuring that participants learn with industry-validated best practices and state-of-the-art tools.
Tailor-made courses
We offer customized training modules that can be aligned with your specific therapeutic area, such as oncology, rare diseases, or vaccine development. Whether you need a focus on small molecule design, protein therapeutics, or real-world evidence analysis, we can adapt the syllabus to meet your team’s unique technical requirements.
Course duration: 5 days
Training fee: USD 1500
Module 1: Foundations of ML in the Pharma Pipeline
- Overview of traditional vs. AI-driven drug discovery cycles
- Identifying "bottleneck" stages in R&D suitable for machine learning intervention
- Cost-benefit analysis of implementing AI in pharmaceutical research
- Introduction to the ecosystem: Python libraries (RDKit, DeepChem) and platforms
- The role of data-driven decision making in reducing late-stage clinical failures
- Practical session: Mapping a drug discovery workflow and identifying data assets for ML modeling
Module 2: Data Engineering and Molecular Representations
- Converting chemical structures to computer-readable formats: SMILES, InChI, and Fingerprints
- Advanced molecular descriptors and feature engineering for chemistry
- Graph-based representations: Treating molecules as nodes and edges
- Handling noisy, sparse, and imbalanced biological datasets
- Data cleaning protocols for public databases like ChEMBL and PubChem
- Practical session: Using RDKit to clean chemical datasets and generate molecular fingerprints
Module 3: Virtual Screening and Hit Identification
- High-throughput virtual screening (HTVS) using machine learning classifiers
- Ligand-based vs. structure-based virtual screening approaches
- Applying Random Forest and Support Vector Machines to bioactivity prediction
- Managing "chemical space" exploration and diversity in hit selection
- Evaluating screening performance: Enrichment factors and AUC-ROC curves
- Practical session: Building a predictive model to identify potential inhibitors for a specific protein target
Module 4: Predictive Modeling for ADMET and Toxicity
- Modeling Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET)
- Quantitative Structure-Activity Relationship (QSAR) modeling fundamentals
- Predicting blood-brain barrier permeability and metabolic stability
- Using Deep Learning to identify potential off-target effects and side effects
- Multi-task learning for simultaneous prediction of multiple ADMET properties
- Practical session: Training a neural network to predict the toxicity levels of a library of compounds
Module 5: De Novo Drug Design and Generative AI
- Introduction to Generative Adversarial Networks (GANs) for molecular generation
- Utilizing Variational Autoencoders (VAEs) for exploring latent chemical space
- Reinforcement learning for optimizing molecules toward specific properties
- Fragment-based generation and scaffold hopping using AI
- Assessing "druggability" and synthetic accessibility of AI-generated molecules
- Practical session: Using a generative model to design novel molecules with specific desired binding affinities
Module 6: Target Identification and Multi-Omics Integration
- Mining scientific literature using Natural Language Processing (NLP) for target discovery
- Integrating genomics, proteomics, and transcriptomics data for biomarker identification
- Network pharmacology: Using Graph Neural Networks to map drug-target-disease interactions
- Patient stratification: Using unsupervised learning to identify disease subtypes
- Identifying novel targets through gene expression profile analysis
- Practical session: Implementing a clustering algorithm to stratify cancer patients based on multi-omics data
Module 7: AI-Driven Lead Optimization and Synthesis Prediction
- Strategies for iterative lead optimization using Bayesian optimization
- Retrosynthesis planning: Using AI to predict efficient chemical synthetic routes
- Machine learning for reaction condition optimization and yield prediction
- Balancing potency, selectivity, and physical properties in lead compounds
- Automated synthesis platforms and their integration with AI co-pilots
- Practical session: Running a retrosynthesis prediction for an AI-designed drug candidate
Module 8: AI in Clinical Trials and Digital Twins
- Patient recruitment and retention: Optimizing trial site selection using predictive analytics
- Creating "Digital Twins" of patient cohorts for in silico trial simulations
- Real-world evidence (RWE) analysis: Mining EHR data for post-market surveillance
- Monitoring patient safety through AI-driven adverse event detection
- Optimizing clinical trial protocols and adaptive trial designs
- Practical session: Simulating a clinical trial outcome using a historical patient dataset
Module 9: Model Interpretability and Explainable AI (XAI)
- Why "Black Box" models fail in clinical and regulatory environments
- Implementing SHAP and LIME for local and global model explanations
- Visualizing chemical features that drive model predictions
- Bridging the gap: Translating ML outputs into chemical and biological intuition
- Dealing with model uncertainty: Using conformal prediction in drug design
- Practical session: Generating an explanation report for a toxicity model to identify "toxicophores"
Module 10: Regulatory Compliance and MLOps for Pharma
- Navigating FDA and EMA guidelines for AI-assisted drug development
- Implementing GxP (Good Practice) standards in machine learning pipelines
- Model versioning, reproducibility, and audit trails for regulatory submissions
- Data privacy and security: Managing sensitive patient and proprietary data
- The future of AI in pharma: Personalized medicine and autonomous labs
- Practical session: Setting up an MLflow project to track experiments and ensure reproducibility for a regulatory audit
Requirements:
- Participants should be reasonably proficient in English.
- Applicants must live up to Armstrong Global Institute admission criteria.
Terms and Conditions
1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free
2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.
3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.
4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.
5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.
Booking for Training
Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.
Or call us on +254720272325 / +254725012095 / +254724452588
Payment Options
We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:
1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.
2. Invoice: We can send a bill directly to you or your company.
3. Deposit directly into Bank Account (Account details provided upon request)
Cancellation Policy
1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.
2. Participants may cancel attendance 14 days or more prior to the training commencement date.
3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.
Tailor Made Courses
This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com
Accommodation and Airport Transfer
Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588
