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Machine Learning for Drug Discovery

Monday, June 30, 2025 - fully remote

In an era marked by remarkable strides in science and technology, drug discovery remains an increasingly formidable endeavor. Historically low success rates in developing new therapeutics, compounded by a steady decline in productivity, pose significant challenges. Over the past decade, the machine learning (ML) community has shown growing interest and embarked on pioneering work, yet many of these efforts remain nascent.

MLDD aims to bring together ML for drug discovery experts, innovators, and enthusiasts from the machine learning, biotechnology and drug discovery domains to converge, exchange ideas, and forge new paths in revolutionizing therapeutic development.

The event is open for all in the community kindly sponsored by GSK plc. See you there!

Keynotes

Charlotte Bunne

EPFL

John Chodera

Achira / MSKCC

Kexin Huang

Stanford University

Jacob Kimmel

NewLimit

Marinka Zitnik

Harvard University

Schedule

All times UK local time (GMT+1)

Time	Title	Presenter
12.45 - 13.00	Opening remarks and introduction	MLDD organisers
13.00 - 13.54	Presentation and Q&A	Charlotte Bunne (EPFL)
13.45 - 14.30	Presentation and Q&A	Marinka Zitnik (Harvard University)
14.30 - 15.15	Presentation and Q&A	John Chodera (Achira, MSKCC)
15.15 - 15.30	Break	All
15.30 - 16.15	Presentation and Q&A	Kexin Huang (Stanford University)
16.15 - 17.00	Presentation and Q&A	Jacob Kimmel (NewLimit)
17.00 - 17.30	Closing remarks and survey results	MLDD organisers

How to attend?

The event will be held fully remote/online this year. Anyone is welcome to call in.

The video stream can be accessed on Youtube here on the day of the event.

Annual MLDD community survey

Are we close to Bio-AGI? What is the most promising research direction in ML for drug discovery?

This year we will - for the first time - run a MLDD community survey to answer these and many other questions reflecting the current feeling of those in the field. Survey results will be discussed at the event.

Please record your response before the event at this link.