Accelerating Drug Discovery from Virtual Screening to Lead Optimization

Drug discovery teams face growing pressure to shorten timelines, improve hit-to-lead progression and manage the rising complexity of data across computational and experimental workflows.

Join this webinar to see how AI-driven computational tools and collaborative data environments can overcome these challenges by streamlining virtual screening, molecular design and data management.

The featured speakers will examine an AI-powered quantum chemistry platform for photocatalyst discovery using generative biology and machine learning approaches.

Attendees will explore methods for validating computationally discovered candidates and translating them into functional assays.

The session will also show how a digital design environment enables teams to digitally design molecules, access and analyze experimental and virtual data and collaborate within a single workspace.

The speakers will also explain how a collaborative database supports protocol setup and assay data organization, linking experimental systems with data management workflows.

Case studies will illustrate successful implementations with measurable improvements in discovery timelines and hit-to-lead progression.

Register for this webinar to learn how integrated computational and data management approaches can accelerate drug discovery from virtual screening to lead optimization.

Keywords: AI, Bioinformatics, Computational Modeling and Simulation, Drug Candidate, Drug Discovery, Drug Screening, Hit to Lead, In Silico, Lead Optimization, Medicinal Chemistry, Other Software