Valence Labs Courses

Artificial Chemical Intelligence: AI for Chemistry and Chemistry for AI

2 ratings

Explore the integration of AI with chemistry, focusing on trustworthy frameworks for molecular discovery. Learn about innovative approaches to predict mechanisms and overcome challenges in computational chemistry.

• YouTube
• 1 hour 1 minute
• Self-Paced
• Free Video

Spatiotemporal Modeling of Molecular Holograms

Explore Spateo, a 3D spatiotemporal modeling framework for embryogenesis, enabling molecular hologram creation, multi-level biological analysis, and insights into organ development through advanced computational techniques.

• YouTube
• 1 hour 9 minutes
• Self-Paced
• Free Video

Distilling Foundation Models via Energy Hessians for Fast, Specialized Machine Learning Force Fields

Explore the distillation of foundation models for faster, specialized Machine Learning Force Fields in computational chemistry, focusing on energy Hessians to maintain accuracy while improving speed.

• YouTube
• 54 minutes
• Self-Paced
• Free Video

A General Framework for Inference-time Scaling and Steering of Diffusion Models

Explore a general framework for inference-time scaling and steering of diffusion models, enabling user-specified properties without expensive fine-tuning through the Feynman Kac steering method.

• YouTube
• 1 hour 18 minutes
• Self-Paced
• Free Video

HELM: Hierarchical Encoding for mRNA Language Modeling

Dive into hierarchical encoding for mRNA language modeling, exploring how HELM's novel pre-training strategy incorporates codon-level structure to improve biological sequence analysis and outperform standard models.

• YouTube
• 59 minutes
• Self-Paced
• Free Video

Integration of Variant Annotations Using Deep Set Networks Boosts Rare Variant Association Testing

Explore DeepRVAT, a neural network model that enhances rare variant association testing by learning gene impairment scores from variant annotations, improving gene discovery and genetic risk detection for human diseases.

• YouTube
• 1 hour 9 minutes
• Self-Paced
• Free Video

Identifying Perturbation Targets Through Causal Differential Networks

Discover how to identify biological intervention targets using causal differential networks for drug discovery and cell engineering applications.

• YouTube
• 56 minutes
• Self-Paced
• Free Video

Genome Modeling and Design Across All Domains of Life with Evo 2

Explore Evo 2, a biological foundation model trained on 9.3 trillion DNA base pairs that predicts genetic variation impacts and generates genome-scale sequences with unprecedented accuracy across all domains of life.

• YouTube
• 59 minutes
• Self-Paced
• Free Video

Single-cell Multiomics Data Integration and Generation with scPairing

Explore single-cell multiomics data integration with scPairing, a variational autoencoder model that embeds different cellular modalities into a common space, enabling generation of novel multiomics data without costly technologies.

• YouTube
• 56 minutes
• Self-Paced
• Free Video

Orb-v3: Atomistic Simulation at Scale

Explore Orb-v3, a next-generation universal interatomic potential that expands the performance-speed-memory frontier with 10x reduced latency and 8x reduced memory while maintaining near state-of-the-art accuracy for atomic simulations.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

Atom Level Enzyme Active Site Scaffolding Using RFdiffusion2

Discover how RFdiffusion2 enables atom-level enzyme design by generating protein structures that accurately position catalytic residues, overcoming previous limitations in de novo enzyme design.

• YouTube
• 1 hour 13 minutes
• Self-Paced
• Free Video

FEAT: Free Energy Estimators with Adaptive Transport

Explore free energy estimation through the FEAT framework, which uses adaptive transport to provide consistent estimators and variational bounds, unifying equilibrium and non-equilibrium methods for neural free energy calculations.

• YouTube
• 1 hour 1 minute
• Self-Paced
• Free Video

Mapping Cells Through Time and Space with Moscot

Explore the moscot framework for single-cell genomics, enabling multimodal analysis across temporal and spatial dimensions to reconstruct developmental trajectories and uncover spatiotemporal dynamics in biological systems.

• YouTube
• 1 hour 8 minutes
• Self-Paced
• Free Video

Quantifying Cell Identity and Tracking Cell Fate Trajectories Using scTOP

Explore scTOP, a physics-inspired approach for quantifying cell identity in single-cell RNA sequencing data without feature selection or dimensional reduction, enabling accurate cell classification and visualization of developmental trajectories.

• YouTube
• 47 minutes
• Self-Paced
• Free Video

Learning Equivariant Non-Local Electron Density Functionals

Delve into advanced machine learning approaches for improving density functional theory through equivariant graph neural networks, focusing on non-local exchange-correlation functionals and molecular energy predictions.

• YouTube
• 48 minutes
• Self-Paced
• Free Video