AI in Pharmaceuticals Series | Part II
AI Can Generate a Molecule in Seconds. Deciding Whether It Deserves the Next Experiment Is the Harder Part.
Artificial intelligence is moving rapidly into pharmaceutical discovery, helping scientists interrogate biological evidence, prioritize targets, screen chemical space, predict molecular properties and propose entirely new structures.
But a convincing prediction is not automatically a useful discovery decision. A highly ranked compound may sit outside the model's reliable chemical space. A generated molecule may look attractive computationally while creating problems with synthesis, selectivity, metabolism or developability. Docking scores can disagree with experimental behaviour. ADMET models can produce conflicting predictions for the same structure. Optimizing one property may quietly worsen several others.
For pharmaceutical R&D teams, the important question is therefore no longer simply what AI can predict, but how those predictions should influence real discovery decisions.
This advanced training examines how AI can be applied from target and compound prioritization through molecular design, predictive modelling, ADMET assessment and lead optimization. The emphasis remains firmly on scientific interpretation, chemical plausibility, prediction reliability, experimental confirmation and practical decision-making. Rather than treating AI as a replacement for medicinal chemistry or discovery expertise, participants will see how computational evidence can be combined with experienced scientific judgement to decide what should be designed, tested, synthesized, deprioritized or investigated next.
Why Attend?
Make better AI-supported discovery decisions by connecting predictions with experimental priorities.
Challenge unreliable model outputs before they influence compound selection or development direction.
Evaluate generated molecules critically beyond novelty, potency predictions and attractive structures.
Interpret ADMET predictions together rather than treating individual endpoints in isolation.
Improve lead prioritization decisions across potency, selectivity, safety and developability.
Evaluate AI tools scientifically based on data, applicability and intended discovery use.
Who Should Attend?
This advanced training is particularly relevant for:
Medicinal chemistry and drug discovery scientists
Computational chemistry and CADD specialists
Pharmaceutical discovery and early R&D teams
DMPK, ADME and toxicology scientists
Molecular modelling and cheminformatics professionals
Discovery biology and target sciences teams
Data scientists supporting pharmaceutical discovery
AI and digital R&D specialists
Pharmaceutical innovation and technology teams
R&D managers evaluating AI-enabled discovery capabilities
Participants should already have a working understanding of pharmaceutical research, medicinal chemistry, drug discovery, biological sciences or related technical disciplines. Elementary drug-discovery concepts will not be the focus of this session.
Training Outline
AI in Pharmaceutical Discovery
Target and Molecular Prioritization
Predictive Modelling and Screening
Generative Molecular Design
ADMET and Developability
Lead Optimization Decisions
Prediction-to-Experiment Workflows
Tools, Platforms and Case Decisions
Bonus: Practical AI & Computational Tools for Drug Discovery
Bonus: AI Drug Discovery Tools: Quick Selection Guide
Expert Q&A Session
Turn AI Predictions Into Better Discovery Decisions
AI can generate targets, structures, rankings and property predictions quickly. The real advantage comes from knowing which outputs deserve confidence, which require challenge, and which experiment should happen next.
Build the confidence to evaluate AI critically, prioritize better compounds and make stronger pharmaceutical discovery decisions. Register NOW!
If you are looking for broader practical coverage of AI across pharmaceutical R&D, formulation, Quality, Regulatory Affairs, scientific research and technical workflows?
Continue the AI in Pharmaceuticals Series. Explore Part I of the AI in Pharmaceuticals Series: AI in Pharmaceuticals: How R&D, Quality & Regulatory Teams Are Using It
Part I and Part II are designed to complement each other, but each training can also be attended independently.
