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Molecular Modelling and Drug Discovery

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by Valence Discovery

60 episodes

Updated Daily

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Podcast Overview

Welcome to this space dedicated to the M2D2 Talks co-organized by Valence Discovery and Mila - Quebec AI Institute. From applied research papers to open source projects, we're hoping to use these talks to help demystify AI for drug discovery and make the field more accessible for newcomers. M2D2 will bring our vibrant AI & drug discovery communities together and spark new perspectives, provoke discussions, and offer a safe space to share new ideas. For the best experience, please visit our YouTube channel where slides and video presentations can be referenced.

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Publishing Since

1/19/2022

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Recent Episodes

June 20, 2023

Structure-Independent Peptide Binder Design via Generative Language Models | Pranam Chatterjee

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our <a href="https://www.youtube.com/watch?v=OfdWayFibBU&ab_channel=ValenceDiscovery">⁠⁠⁠⁠⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠⁠⁠⁠⁠</a>to see the presented slides. Try datamol.io - the open source toolkit that simplifies molecular processing and featurization workflows for machine learning scientists working in drug discovery: <a href="https://datamol.io/">⁠⁠⁠⁠⁠https://datamol.io/⁠⁠⁠⁠⁠</a> If you enjoyed this talk, consider joining the <a href="https://m2d2.io/talks/m2d2/about/">⁠⁠⁠⁠⁠⁠⁠⁠Molecular Modeling and Drug Discovery (M2D2) talks⁠⁠⁠⁠⁠⁠⁠⁠</a> live. Also, consider joining the <a href="https://join.slack.com/t/m2d2group/shared_invite/zt-16w1rjqqs-n81TiK~iB23XbZ0QWMYs~A">⁠⁠⁠⁠⁠⁠⁠⁠M2D2 Slack⁠⁠⁠⁠⁠⁠⁠⁠</a>. Abstract: The ability to modulate pathogenic proteins represents a powerful treatment strategy for diseases. Unfortunately, many proteins are considered “undruggable” by small molecules, and are often intrinsically disordered, precluding the usage of structure-based tools for binder design. To address these challenges, we have developed a suite of algorithms that enable the design of target-specific peptides via protein language model embeddings, without the requirement of 3D structures. First, we train a model that leverages ESM-2 embeddings to efficiently select high-affinity peptides from natural protein interaction interfaces. We experimentally fuse model-derived peptides to E3 ubiquitin ligases and identify candidates exhibiting robust degradation of undruggable targets in human cells. Next, we develop a high-accuracy discriminator, based on the CLIP architecture, to prioritize and screen peptides with selectivity to a specified target protein. As input to the discriminator, we create a Gaussian diffusion generator to sample an ESM-2-based latent space, fine-tuned on experimentally-valid peptide sequences. Finally, to enable de novo generation of binding peptides, we train an instance of GPT-2 with protein interacting sequences to enable peptide generation conditioned on target sequence. Our model demonstrates low perplexities across both existing and generated peptide sequences. Together, our work lays the foundation for programmable protein targeting and editing applications. Speaker: <a href="https://www.linkedin.com/in/pranamanam/" target="_blank" rel="noopener noreferer">Pranam Chatterjee</a> Twitter - <a href="https://twitter.com/tossouprudencio">⁠⁠⁠⁠⁠⁠⁠⁠Prudencio⁠⁠⁠⁠⁠⁠⁠⁠</a> Twitter - <a href="https://twitter.com/hsu_jonny">⁠⁠⁠⁠⁠⁠⁠⁠Jonny⁠⁠⁠⁠⁠⁠⁠⁠</a> Twitter - ⁠⁠⁠<a href="https://twitter.com/datamol_io/status/1643263399915311104?s=20">⁠⁠⁠⁠⁠datamol.io</a>

June 13, 2023

Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics | Albert Musaelian

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our <a href="https://www.youtube.com/watch?v=OfdWayFibBU&ab_channel=ValenceDiscovery">⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠⁠⁠⁠⁠⁠</a>to see the presented slides. Try datamol.io - the open source toolkit that simplifies molecular processing and featurization workflows for machine learning scientists working in drug discovery: <a href="https://datamol.io/">⁠⁠⁠⁠⁠⁠https://datamol.io/⁠⁠⁠⁠⁠⁠</a> If you enjoyed this talk, consider joining the <a href="https://m2d2.io/talks/m2d2/about/">⁠⁠⁠⁠⁠⁠⁠⁠⁠Molecular Modeling and Drug Discovery (M2D2) talks⁠⁠⁠⁠⁠⁠⁠⁠⁠</a> live. Also, consider joining the <a href="https://join.slack.com/t/m2d2group/shared_invite/zt-16w1rjqqs-n81TiK~iB23XbZ0QWMYs~A">⁠⁠⁠⁠⁠⁠⁠⁠⁠M2D2 Slack⁠⁠⁠⁠⁠⁠⁠⁠⁠</a>. Abstract: Trade-offs between accuracy and speed have long limited the applications of machine learning interatomic potentials. Recently, E(3)-equivariant architectures have demonstrated leading accuracy, data efficiency, transferability, and simulation stability, but their computational cost and scaling has generally reinforced this trade-off. In particular, the ubiquitous use of message passing architectures has precluded the extension of accessible length- and time-scales with efficient multi-GPU calculations. In this talk I will discuss Allegro, a strictly local equivariant deep learning interatomic potential designed for parallel scalability and increased computational efficiency that simultaneously exhibits excellent accuracy. After presenting the architecture, I will discuss applications and benchmarks on various materials and chemical systems, including recent demonstrations of scaling to large all-atom biomolecular systems such as solvated proteins and a 44 million atom model of the HIV capsid. Finally, I will summarize the software ecosystem and tooling around Allegro. Speaker: <a href="https://www.krellinst.org/csgf/fellows/profile?n=musaelian2020" target="_blank" rel="noopener noreferer">Albert Musaelian</a> Twitter - <a href="https://twitter.com/tossouprudencio">⁠⁠⁠⁠⁠⁠⁠⁠⁠Prudencio⁠⁠⁠⁠⁠⁠⁠⁠⁠</a> Twitter - <a href="https://twitter.com/hsu_jonny">⁠⁠⁠⁠⁠⁠⁠⁠⁠ Jonny⁠⁠⁠⁠⁠⁠⁠⁠⁠</a> Twitter - ⁠⁠⁠<a href="https://twitter.com/datamol_io/status/1643263399915311104?s=20">⁠⁠⁠⁠⁠⁠datamol.io</a>

June 7, 2023

Multimodal Deep Learning for Protein Engineering | Kevin K. Yang

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our <a href="https://www.youtube.com/watch?v=OfdWayFibBU&ab_channel=ValenceDiscovery">⁠⁠⁠⁠⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠⁠⁠⁠⁠</a>to see the presented slides. Try datamol.io - the open source toolkit that simplifies molecular processing and featurization workflows for machine learning scientists working in drug discovery: <a href="https://datamol.io/">⁠⁠⁠⁠⁠https://datamol.io/⁠⁠⁠⁠⁠</a> If you enjoyed this talk, consider joining the <a href="https://m2d2.io/talks/m2d2/about/">⁠⁠⁠⁠⁠⁠⁠⁠Molecular Modeling and Drug Discovery (M2D2) talks⁠⁠⁠⁠⁠⁠⁠⁠</a> live. Also, consider joining the <a href="https://join.slack.com/t/m2d2group/shared_invite/zt-16w1rjqqs-n81TiK~iB23XbZ0QWMYs~A">⁠⁠⁠⁠⁠⁠⁠⁠M2D2 Slack⁠⁠⁠⁠⁠⁠⁠⁠</a>. Abstract: Engineered proteins play increasingly essential roles in industries and applications spanning pharmaceuticals, agriculture, specialty chemicals, and fuel. Machine learning could enable an unprecedented level of control in protein engineering for therapeutic and industrial applications. Large self-supervised models pretrained on millions of protein sequences have recently gained popularity in generating embeddings of protein sequences for protein property prediction. However, protein datasets contain information in addition to sequence that can improve model performance. This talk will cover models that use sequences, structures, and biophysical features to predict protein function or to generate functional proteins. Speaker: <a href="https://twitter.com/KevinKaichuang" target="_blank" rel="noopener noreferer">Kevin K. Yang</a> Twitter - <a href="https://twitter.com/tossouprudencio">⁠⁠⁠⁠⁠⁠⁠⁠Prudencio⁠⁠⁠⁠⁠⁠⁠⁠</a> Twitter - <a href="https://twitter.com/hsu_jonny">⁠⁠⁠⁠⁠⁠⁠⁠Jonny⁠⁠⁠⁠⁠⁠⁠⁠</a> Twitter - ⁠⁠⁠<a href="https://twitter.com/datamol_io/status/1643263399915311104?s=20">⁠⁠⁠⁠⁠datamol.io</a>

60 total episodes available

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Frequently asked questions

Have a different question and can't find the answer you're looking for? Reach out to our support team by sending us an email and we'll get back to you as soon as we can.

What is Molecular Modelling and Drug Discovery?: Welcome to this space dedicated to the M2D2 Talks co-organized by Valence Discovery and Mila - Quebec AI Institute.

From applied research papers to open source projects, we're hoping to use these talks to help demystify AI for drug discovery and make the field more accessible for newcomers. M2D2 will bring our vibrant AI & drug discovery communities together and spark new perspectives, provoke discussions, and offer a safe space to share new ideas.

For the best experience, please visit our YouTube channel where slides and video presentations can be referenced.
How often does this podcast release new episodes?: This podcast updates daily.
Where can I listen to this podcast?: This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
Does this podcast accept guests?: Yes, this podcast regularly features guests.

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