01:17:00

Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold | Lazar Atanackovic

01:01:43

Derivative-Free Guidance in Continuous and Discrete Diffusion Models | Xiner Li and Masatoshi Uehara

01:29:26

A long-context RNA foundation model for predicting transcriptome architecture | Ali Saberi

01:15:53

Fine-tuning Flow and Diffusion Generative Models | Carles Domingo-Enrich

01:21:48

Probabilistic Inference in Language Models via Twisted Sequential Monte | Rob Brekelmans

01:47

MolGPS - A Foundational GNN for Molecular Property Prediction

01:15:31

Geometric deep learning framework for de novo genome assembly | Lovro Vrček

01:07:02

An Open MetaGenomic corpus for mixed-modality genomic language modeling | Andre Cornman

04:20

Propensity Score Alignment of Unpaired Multimodal Data

01:27:22

Tokenized and Continuous Embedding Compressions of Protein Sequence and Structure | Amy X. Lu

01:34:17

Discrete Flow Matching | Andrew Campbell

13:54

Day 5 - Introducing Bioptic | Vlad Vinograv

23:49

Day 5 - Hackathon Introduction | Cas Wognum

59:45

Day 5 - Open-Source Initiatives & Benchmarking Efforts | Karmen Condic-Jurkic

01:05:32

Day 5 - Protein Folding & Design | Alex Tong

57:04

Day 5 - LLMs in Drug Discovery | Andres M Bran

01:31:15

Lab 4 - Target Deconvolution Explanation | Ali Denton & Kristina Ulicna

01:02:48

Day 4 - Modeling Population Dynamics | Charlotte Bunne

01:06:58

Day 4 - Causal Discovery & Representation Learning | Jason Hartford

56:54

Day 4 - Multimodal Omics & AI | Sébastien Lemieux

01:00:34

Day 4 - Phenomics in Drug Discovery: Microscopy and Machine Learning | Anne Carpenter

01:28:13

Lab 3 - De Novo Generation Explanation

01:08:23

Day 3 - Harnessing Geometric ML for Molecular Design | Michael Bronstein

01:04:22

Day 3 - Synthesizability & Molecular Synthesis | Connor Coley

01:06:19

Day 3 - Exploring Molecular Space & Active Learning | Yoshua Bengio

54:53

Day 3 - Generative Models of Molecular Structures | Camille Bilodeau

01:09:46

Lab 2 - Binding Affinity Prediction with ML Based Docking

26:09

Lab 2 - Binding Affinity Prediction with ML Based Docking Explanation

57:04

Day 2 - Coarse Grained Biological Systems | Jacopo Venturin

01:01:06

Day 2 - Accelerate Atomistic Simulations, Sampling, and Dynamics | Pratyush Tiwary

51:39

Day 2 - Learning ML Interatomic Potentials | Gianni De Fabritiis

57:33

Day 2 - ML in Structure Based Drug Discovery | Gabriele Corso

01:22:40

Lab 1 - Virtual Screening

26:29

Lab 1 - Virtual Screening Explanation

45:18

Day 1 - Learning Geometry & 3D Symmetries | Mario Geiger

45:49

Day 1 - Graph Neural Networks for Chemistry | Dominique Beaini

01:05:34

Day 1 - Molecular Representation & Scoring | Emmanuel Noutahi

01:05:54

Day 1 - Intro to ML in Drug Discovery: Principles & Applications | Bharath Ramsundar

15:45

Opening Remarks

01:42

ML4DD Summer School | 2024 Recap

01:44:39

Gene regulatory network structure informs the distribution of perturbation effects | Matthew Aguirre

01:05:12

Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion | Boyuan Chen

45:55

Geometric Deep Learning for Protein Understanding | Jian Tang

06:49

MoML 2024: Opening Remarks | Dominique Beaini & Jonathan Hsu

32:23

Polaris: Industry-Led Initiative to Critically Assess ML for Real-World Drug Discovery | Cas Wognum

44:31

Efficiently Exploring Combinatorial Perturbations From High Dimensional Observation | Jason Hartford

37:57

Leveraging Molecular ML + Property Prediction in Drug Design | Raquel Rodríguez-Pérez

50:28

Towards Rational Drug Design with AlphaFold 3 | Max Jaderberg

51:42

G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding | Xiaoxin He

01:18:34

Designing DNA With Tunable Regulatory Activity Using Discrete Diffusion | Anirban Sarkar & Peter Koo

01:09:50

Metric Flow Matching for Smooth Interpolations on the Data Manifold | Kacper Kapusniak

01:12:12

Scalable molecular simulation of electrolyte solutions with quantum chemical accuracy | Tim Duignan

01:28:17

Approximately Piecewise E(3) Equivariant Point Networks | Matan Atzmon

01:06:02

gRNAde: Geometric Deep Learning for 3D RNA inverse design | Chaitanya K. Joshi

32:00

Contextual AI Models for Single-cell Protein Biology | Michelle Li

32:55

Identifying Representations for Intervention Extrapolation | Sorawit (James) Saengkyongam

55:56

The Continuous Language of Protein Structure | Ben Murrell

59:10

The Future of Chemistry is Self-Driving | Alán Aspuru-Guzik

53:04

Weisfeiler Leman for Euclidean Equivariant Machine Learning | Snir Hordan

54:19

A Causal Inference Framework for Combinatorial Interventions | Anish Agarwal