Computer Vision for Drug Discovery Where Are We and What is Beyond?

This page is for CVPR 2025 workshops on Computer Vision for Drug Discovery: Where Are We and What is Beyond?

Introduction

The workshop aims to bridge the gap between computer vision, artificial intelligence, and the life sciences, with a focus on transformative advancements in drug discovery. By integrating innovative imaging modalities—such as Spatial Transcriptomics, Cell Painting, and Optical Pooled Screening—with state-of-the-art computer vision techniques, this workshop seeks to foster collaboration between experts in biomedical science, AI, and computer vision.

The workshop highlights the potential for revolutionizing drug discovery processes, driving faster and more accurate identification of therapeutic targets, and expediting the development of treatments for complex diseases. Addressing pressing challenges like cancer, neurodegenerative disorders, and pandemics, the focus lies on leveraging AI to analyze high-dimensional biological data, enhancing our understanding of disease mechanisms and responses to therapies.

For the CVPR community, this represents an exciting opportunity to expand beyond traditional image processing tasks into applications with tangible societal impact. By applying computer vision expertise to critical healthcare and pharmaceutical challenges, participants will engage with tasks like multi-modal data fusion, enhancing explainability in biomedical applications, and addressing the unique complexities of biological imaging, such as sparse or noisy datasets.

This workshop is aligned with CVPR’s growing emphasis on “AI for social good,” offering computer vision researchers a platform to contribute to advances in medical science that could improve the lives of millions. It is a call to action for interdisciplinary innovation, uniting diverse expertise to tackle some of the most critical challenges in global health.

Speakers

	Shantanu Singh Broad Institute
	Christina S Leslie Memorial Sloan Kettering Cancer Center
	Juan C Caicedo Morgridge Institute for Research
	Jean Fan Johns Hopkins Biomedical Engineering
	Patrick Schwab GSK
TBD	More to come!

Call for papers:

Two tracks are available: featuring papers for workshop proceedings and posters from recent publications in top conferences like CVPR, ICCV, ECCV, NeurIPS, ICLR, and AAAI, as well as prominent journals such as Nature, Science, and Nature Methods.

The papers must follow the CVPR paper template.

The submission must go through: openreview

Topics

Spatial Transcriptomics for Drug Discovery
Cell Painting and Morphological Profiling
Optical Pooled Screening and Functional Genomics
AI for Multi-modal Biomedical Imaging
Explainable AI and Predictive Modeling in Drug Discovery
Sparse Labelled Data in Drug Discovery Domain
AI-driven Drug Discovery Pipelines
Imaging Biomarkers Discovery:
Safe and Trustworthy Computer Vision for Drug Discovery and Clinics:

Dates

Paper Submission Deadline: March 17 ‘25 07:59 AM UTC

Notification of Acceptance: April 1 ‘25 07:59 AM UTC

Camera-Ready Deadline: April 7 ‘25 07:59 AM UTC

Final workshop program: May 9 ‘25 07:59 AM UTC

Workshop Date: June 12

Challenge:

Overview

In this challenge, you will develop machine learning models for learning representations of molecular perturbations in cellular systems using microscopy imaging data.

Description

Learning representations of molecular perturbations in cells is a crucial task in drug discovery. Having robust representations that capture how certain perturbations affect cells would allow us to rapidly find compounds that present similar effects. However, experimental and other types of variability unrelated to the perturbation’s biological effect make it a challenging task. Therefore, we present a compound classification task where models will be evaluated both in unseen plates and unseen cell lines.

More info can be found at: Challenge and kaggle

Organizing team:

Workshop comitee:

	Dawid Rymarczyk Ardigen SA Jagiellonian University
	Ilknur Icke Novo Nordisk
	Chao-hui Huang Pfizer
	Gayathri Mohankumar AstraZeneca
	Anne Carpenter Broad Institute SyzOnc

Challenge comitee:

	Adriana Borowa Ardigen SA
	Ana Sanchez-Fernandez Johannes Kepler University Linz