AI/ML for Early Drug Discovery – Part 1
AI-Driven Design and Optimization of Small Molecule, Peptide, and Antibody-Based Drugs
4/20/2027 - April 21, 2027 ALL TIMES PDT
Artificial Intelligence (AI) and Machine Learning (ML) are rapidly becoming an integral part of drug discovery, and regulatory agencies are increasingly recognizing their role in drug development and submissions. This two-part conference on AI/ML for Early Drug Discovery examines practical applications of AI/ML, emerging computational technologies, and the data needed to make them work effectively. Part One explores how AI/ML is accelerating drug design, virtual screening, and hit identification and lead optimization across a growing range of therapeutic modalities. Part Two looks at using generative and agentic AI, foundation models, multimodal data, and advanced computational approaches to identify and validate new targets, unravel complex cellular biology, explore vast chemical space, and find new ways to tackle challenging and previously “undruggable” targets. Through real-world applications and candid discussions of both successes and limitations, attendees will gain a clearer understanding of which AI/ML approaches are working today, how they are changing discovery workflows, and where the technology is headed next.
Preliminary Agenda

EMERGING TRENDS IN AI USE FOR DRUG DISCOVERY

Rethinking Molecular AI for Drug Discovery

Photo of Woody Sherman, PhD, Founder and Chief Innovation Officer, PsiThera , Founder and Chief Innovation Officer , PsiThera
Woody Sherman, PhD, Founder and Chief Innovation Officer, PsiThera , Founder and Chief Innovation Officer , PsiThera

AI is impacting drug discovery through agentic workflows and advances in co-folding such as OpenFold3. Yet predicting molecular behavior and properties remains challenging. We explore how to build foundation models with quantum mechanics, molecular dynamics, and experimental feedback to improve predictions of affinity, energetics, properties, and function. Drawing on PsiThera's experience, we discuss how AI is advancing therapeutic discovery while empowering drug designers with direct access to sophisticated computational tools.


Where is the AI in Drug Discovery, and Where Should it be Instead?

Photo of Abraham Heifets, PhD, Former Co-Founder & Former CEO, Atomwise Inc. , Former Co-Founder & Former CEO , Atomwise Inc
Abraham Heifets, PhD, Former Co-Founder & Former CEO, Atomwise Inc. , Former Co-Founder & Former CEO , Atomwise Inc

Collaborative Preclinical AI Drug Discovery

Photo of Vanessa Braunstein, Senior Director, TuneLab AI Drug Discovery Platform, Eli Lilly and Company , Senior Director , TuneLab AI Drug Discovery Platform , Eli Lilly & Co
Vanessa Braunstein, Senior Director, TuneLab AI Drug Discovery Platform, Eli Lilly and Company , Senior Director , TuneLab AI Drug Discovery Platform , Eli Lilly & Co

Designing for Oral Bioavailability from the Start: Zero-Shot ADME Models and Agentic Hit-to-Lead

Photo of Kurt Thorn, PhD, CTO, ArrePath , CTO , ArrePath
Kurt Thorn, PhD, CTO, ArrePath , CTO , ArrePath

ArrePath's oral bioavailability model, trained only on public data, predicts F on series it has never seen. It guided an antibacterial program from ~10% to >80% F, surfacing non-obvious orally bioavailable compounds. Our AI agent runs this model alongside potency and ADME models, so one scientist can evaluate 10,000+ designs a week and prioritize bioavailable compounds from the first design cycle. We present case studies from real drug discovery campaigns.

Engineering Biomolecular Interactions with Physics-Aware AI: From Degraders to Agonistic Biologics

Photo of Dima Kozakov, PhD, Director, AI/Physics in Drug Discovery, The University of Texas at Austin , Director , AI/Physics in Drug Discovery , UT Austin
Dima Kozakov, PhD, Director, AI/Physics in Drug Discovery, The University of Texas at Austin , Director , AI/Physics in Drug Discovery , UT Austin

Panel Moderator:

PANEL DISCUSSION:
Grand Challenges for AI in Drug Discovery: What is Working and What we still Need to Solve

Woody Sherman, PhD, Founder and Chief Innovation Officer, PsiThera , Founder and Chief Innovation Officer , PsiThera

AI/ML FOR PEPTIDE DESIGN & OPTIMIZATION

Building and Using Foundational Structure Prediction Models to Discover and Develop Therapeutic Peptides

Photo of Patrick J. Salveson, PhD, Co-Founder and CTO, Vilya Therapeutics , Co-Founder and CTO , Research & Development , Vilya, Inc.
Patrick J. Salveson, PhD, Co-Founder and CTO, Vilya Therapeutics , Co-Founder and CTO , Research & Development , Vilya, Inc.

Unbiased: Physics-Informed Generative Design of Membrane-Permeable Macrocyclic Peptides against PPI Targets

Photo of Hans Melo, PhD, Co-Founder & CEO, Menten AI , Co Founder & CEO , Menten AI
Hans Melo, PhD, Co-Founder & CEO, Menten AI , Co Founder & CEO , Menten AI

Recent clinical successes have reawakened interest in cyclic peptides. However, simultaneous design of permeability and potency remains a significant challenge. We present a multi-technology approach combining physics-based methods and machine learning to perform multiparameter design, generating cyclic peptides with robust PAMPA permeability and nanomolar potency. Critically, unlike ML-only methods—which are biased by their training sets and largely limited to the 20 canonical amino acids—our approach explores an unbiased chemical space.

From Target Structure to Oral Peptide Leads: AI-Guided Design and Optimization

Photo of Alan Nafiiev, PhD, CEO & Founder, Receptor.AI , CEO & Founder , Receptor.AI
Alan Nafiiev, PhD, CEO & Founder, Receptor.AI , CEO & Founder , Receptor.AI

We present an AI-driven approach to de novo macrocyclic peptide design directly from target structures, aiming to achieve picomolar binding affinity and sufficient metabolic stability while enabling therapeutically relevant exposure even with limited intestinal absorption. Using a validated cytokine receptor as a case study, we demonstrate the progression from binding interface identification to experimentally characterized macrocyclic peptide leads, highlighting the balance between potency, stability, and intestinal absorption.

PLENARY

PLENARY KEYNOTE

The Druggable Transcriptome: From RNA Recognition to Precision Medicines

Matthew D. Disney, PhD, Institute Professor & Chair, UF Scripps Institute for Biomedical Innovation & Technology , Professor , Chemistry , Scripps Research Institute


For more details on the conference, please contact:
Tanuja Koppal, PhD
Senior Conference Director
Cambridge Healthtech Institute
Email: tkoppal@healthtech.com

For sponsorship information, please contact:
Kristin Skahan
Senior Business Development Manager
Cambridge Healthtech Institute
Phone: (+1) 781-972-5431
Email: kskahan@healthtech.com


Register Early for Maximum Savings

APRIL 19

Covalent & Induced Proximity-Based Therapies

RNA-Modulating Small Molecule Drugs

Generative AI for Drug Discovery

training seminars

View In-Person Short Courses

APRIL 20 - 21

Degraders & Molecular Glues - Part 1

Small Molecule Discovery Technologies

AI/ML for Early Drug Discovery - Part 1

Linker & Conjugation Chemistries

Peptides

APRIL 21 - 22

Degraders & Molecular Glues - Part 2

Protein-Protein Interactions / Difficult Targets

AI/ML for Early Drug Discovery - Part 2

DNA-Encoded Libraries

GLP1 & Oral Peptides


Drug Discovery Chemsitry Europe

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