DocGen AI is our innovative clinical document authoring platform, currently expanding to encompass over 60 document types this year. This talk will outline the latest phase of the platform’s evolution, focusing on technical considerations and user experience, particularly regarding the quality of generated sections. We will also present initial metrics on user adoption and productivity improvements.

This talk explores how Novartis leverages generative AI to harness institutional knowledge for accelerating drug discovery. By integrating data science and AI-driven insights, this approach unearths critical knowledge assets, streamlining research processes and enhancing decision-making. Attendees will gain an understanding of how AI tools facilitate knowledge discovery, reduce time-to-insight, and drive innovation in pharmaceutical research, ultimately advancing the discovery of effective new therapies.

This talk delves into AI-driven approaches for designing and predicting molecular structures, optimizing drug discovery processes, and uncovering novel therapeutic candidates. Attendees will gain insights into generative AI's transformative role in R&D, enhancing precision and efficiency in molecular and pharmaceutical sciences.

Introducing a framework leveraging GenAI and Large Language Models (LLMs) to identify and mitigate risks in pharmaceutical supply chains and manufacturing. By integrating an agentic layer, the framework enhances competitive intelligence, strengthens resilience, and optimizes audit efficiency. It replaces siloed pipelines and analytics with seamless reporting and alerting. Additionally, the framework extends to clinical trials, improving process workflows, and providing comprehensive risk mitigation across diverse areas, ensuring long-term operational success.

The traditional drug discovery process is lengthy, costly, and fraught with failure, with development often taking over a decade and costing billions. This talk will explore how generative AI is transforming this paradigm through Insilico Medicine’s innovative machine learning platform, enabling novel target identification, small molecule design, and clinical trial prediction. Discover how generative AI is accelerating drug development and reshaping the future of medicine.

Drug development is inefficient and costly, mainly due to a lack of available clinical data to inform programs earlier and prevent late-stage failures. Iambic has created a multimodal and multitask transformer model, called Enchant, trained on abundant discovery data and limited amounts of clinical data to break through this data wall and predict clinical outcomes. Enchant provides insights into key clinical properties of potential medicines, reducing clinical risk by predicting the viability of molecules from the earliest stages of discovery.

Montai is advancing CONECTA, an integrated platform that enables discovery of new oral small molecule therapeutics against chronic disease from a diverse and bioactive chemical space generated from natural products with a history of human consumption. CONECTA integrates proprietary bioassay data and machine learning models built on repositories of chemistry and biological data on key pathways and drug-like properties. This integration of machine learning with chemistry and biology expertise enables better decision-making and improves the probability of small molecule success before any compounds are tested in the lab.