Redefining Drug Discovery with Artificial Intelligence

Artificial intelligence is rapidly transforming drug discovery, with applications ranging from hit identification and lead optimization to the design of entirely new therapeutic molecules. Yet important questions remain: Which approaches are delivering real-world results? How are researchers integrating machine learning with experimental biology and chemistry? And what challenges still stand between promising algorithms and life-changing medicines?

Join ACS Webinars on September 16, 2026, from 11:00 AM – 12:30 PM ET for an engaging discussion titled AI-Guided Molecular Design: From Biological Programming to Drug Discovery, and focused on how AI is accelerating innovation across the drug discovery pipeline. Moderated by Jon Stokes (McMaster University), a pioneer in AI-driven antibiotic discovery, this webinar brings together leading researchers who are applying cutting-edge computational approaches to some of the most pressing challenges in medicine.

From Molecular Design to Therapeutic Discovery

Recent advances in generative AI have moved beyond analyzing biological data to designing new molecules with desired properties. Pranam Chatterjee, Assistant Professor at the University of Pennsylvania, will discuss how his laboratory develops generative models capable of designing peptides, proteins, mRNAs, and other biologics. Using protein language models, diffusion methods, and flow-matching frameworks these approaches optimize critical therapeutic properties such as specificity, solubility, safety, and half-life. Attendees will also learn how emerging AI frameworks are beginning to model biological trajectories themselves, opening possibilities for controlling protein and cell states in entirely new ways.

The webinar will also explore how AI and computational biophysics can reveal the hidden mechanisms that govern molecular recognition. Shozeb Haider of University College London will present work combining molecular simulations, machine learning, kinetics, and structural biology to understand how inhibitors recognize and engage β-lactamase targets associated with antimicrobial resistance. These insights have enabled the design of next-generation inhibitors with enhanced potency and faster target engagement.

Completing the panel, Alexey Zakharov from the National Center for Advancing Translational Sciences (NCATS) will discuss AI-driven workflows for hit discovery and optimization. His presentation will highlight strategies that combine high-throughput screening data with machine learning, scaffold-centric exploration, and reaction-based SAR modeling to efficiently navigate enormous virtual chemical libraries and identify potent, synthetically accessible lead compounds.

What You'll Learn

• How generative AI is being used to design therapeutically relevant biologics and other molecular modalities.
• How machine learning and molecular simulations can reveal mechanistic insights that drive rational drug design.
• Strategies for integrating AI with high-throughput screening, SAR modeling, and virtual screening to accelerate lead discovery and optimization.
• Opportunities and challenges in translating AI innovations into experimentally validated therapeutic candidates.

AI is rapidly evolving from a tool for prediction into a platform for molecular invention. Join us to hear from researchers at the forefront of this transformation and discover how machine learning, computational biology, and cheminformatics are helping shape the future of drug discovery.

For this webinar, ACS Publications has curated a collection of recent, high-impact articles that showcase how generative AI, machine learning, and computational modeling are advancing drug discovery, therapeutic design, and the development of next-generation medicines, including research on:

• AI-driven target identification and drug discovery
• Generative AI for molecular design and lead optimization
• Computational modeling of drug–target interactions
• Machine learning for drug safety and efficacy prediction
• AI-enabled biologics, bioconjugates, and advanced therapeutics
• Data-driven drug delivery and formulation design
• Autonomous and end-to-end AI drug development workflows

Explore Recent AI-Related Research from ACS Journals

SAMTI: Sampling Adaptive Thermodynamic Integration for Alchemical Free Energy Calculations

Design of Carbon Nanotube Inhibitors for Main Proteinase of SARS-CoV-2: A Combined Deep Learning and Molecular Dynamics Simulation Study

Single-Molecule Bioelectronic Sensors with AI-Aided Data Analysis: Convergence and Challenges

Stimuli-Responsive Smart Polymer: A Precise Era with Artificial Intelligence and Machine Learning

Harnessing Machine Learning for the Virtual Screening of Natural Compounds as Both EGFR and HER2 Inhibitors in Colorectal Cancer: A Novel Therapeutic Approach

Machine Learning-Driven Drug Repurposing for KRAS G12C and KRAS G12D Inhibition

Drug Release Nanoparticle System Design: Data Set Compilation and Machine Learning Modeling

Combating Counterfeit Drugs via Machine Learning-Enabled Array Screening of Multilayer Evolutionary Combinatorial Libraries

Machine-Learning Framework to Predict the Performance of Lipid Nanoparticles for Nucleic Acid Delivery

Predicting Protein Function in the AI and Big Data Era

From First Principles to Function: How AI Is Reshaping Enzyme Design

Artificial Intelligence for Discovery in Life Sciences

Decoding of Inconsistent Biological Data: A Critical Step toward Enhanced AI Predictivity in Drug Discovery

AI-Enabled Ultra-large Virtual Screening Identifies Potential Inhibitors of Choline Acetyltransferase for Theranostic Purposes

Accelerating the Discovery of Abiotic Vesicles with AI-Guided Automated Experimentation

AI Drug Discovery: Expanding the Horizons of Infectious Disease Therapeutics

Identification of a Novel Indolizine RORγT Inverse Agonist Using the AI-Driven Drug Design Platform

D2Screen: Embedding Pretrained Representation Learning Model and Molecular Docking for Virtual Screening

Machine Learning-Guided Design of Rhenium Tricarbonyl Complexes for Next-Generation Antibiotics

Cyclic Peptide Therapeutic Agents Discovery: Computational and Artificial Intelligence-Driven Strategies

Thinking on the Use of Artificial Intelligence in Drug Discovery

AIoptamer: Artificial Intelligence-Driven Aptamer Optimization Pipeline for Targeted Therapeutics in Healthcare

3D Bioprinting and Artificial Intelligence for Tumor Microenvironment Modeling: A Scoping Review of Models, Methods, and Integration Pathways

DeepDOX1: A Dual-Drive Framework Integrating Deep Learning and First-Principles Quantum Chemistry for Drug–Protein Affinity Prediction

Harnessing AI for Antimicrobial Peptide Innovation against Multidrug Resistance

Context-Aware Multilevel Classification of Semantic Relations in Drug-Adverse Drug Reaction (ADR) Networks–Predicting Drug-Induced Liver Injury (DILI) as a Case Study

From Prompt to Drug: Toward Pharmaceutical Superintelligence

AI-Driven Integration of Transcriptomics, Quantum Mechanics, and Physiology for Predicting Drug-Induced Liver Injury in Data-Limited Scenarios

Evaluating BindCraft for Generative Design of High-Affinity Peptides

Structure Prediction and Protein Engineering Yield New Insights into Microcin J25 Precursor Recognition

Recent Advances in Machine Learning Models for Predicting Toxicity of Inorganic Nanoparticles

Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies

Machine Learning for Prediction and Synthesis of Anion Exchange Membranes

Machine Learning-Assisted Design of Advanced Polymeric Materials

Recent Advances in Implementation of Machine Learning for Environmental Nontarget Identification

Evaluation of Machine Learning Based QSAR Models for the Classification of Lung Surfactant Inhibitors

Speculum-Free Biomaterials, Multi-omics Liquid Biopsy, and AI-Driven Theranostic Imaging for Ovarian Cancer Detection