[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠⁠⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠⁠⁠⁠⁠to see the presented slides.

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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: Pranam Chatterjee

Twitter -  ⁠⁠⁠⁠⁠⁠⁠⁠Prudencio⁠⁠⁠⁠⁠⁠⁠⁠

Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠Jonny⁠⁠⁠⁠⁠⁠⁠⁠

Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠datamol.io

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠⁠⁠⁠⁠⁠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: ⁠⁠⁠⁠⁠⁠https://datamol.io/⁠⁠⁠⁠⁠⁠

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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.

Speaker: Albert Musaelian

Twitter -  ⁠⁠⁠⁠⁠⁠⁠⁠⁠Prudencio⁠⁠⁠⁠⁠⁠⁠⁠⁠

Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠ Jonny⁠⁠⁠⁠⁠⁠⁠⁠⁠

Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠datamol.io

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠⁠⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠⁠⁠⁠⁠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: ⁠⁠⁠⁠⁠https://datamol.io/⁠⁠⁠⁠⁠

If you enjoyed this talk, consider joining the ⁠⁠⁠⁠⁠⁠⁠⁠Molecular Modeling and Drug Discovery (M2D2) talks⁠⁠⁠⁠⁠⁠⁠⁠ live.

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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: Kevin K. Yang

Twitter -  ⁠⁠⁠⁠⁠⁠⁠⁠Prudencio⁠⁠⁠⁠⁠⁠⁠⁠

Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠Jonny⁠⁠⁠⁠⁠⁠⁠⁠

Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠datamol.io

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠⁠⁠⁠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: ⁠⁠⁠⁠https://datamol.io/⁠⁠⁠⁠

If you enjoyed this talk, consider joining the ⁠⁠⁠⁠⁠⁠⁠Molecular Modeling and Drug Discovery (M2D2) talks⁠⁠⁠⁠⁠⁠⁠ live.

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Abstract: Molecular simulations enable the study of biomolecules and their dynamics on an atomistic scale. A common task is to compare several simulation conditions - like mutations or different ligands - to find significant differences and interrelations between them. However, the large amount of data produced for ever larger and more complex systems often renders it difficult to identify the structural features that are relevant for a particular phenomenon. PENSA is a flexible software package that enables a comprehensive and thorough investigation into biomolecular conformational ensembles. It provides a wide variety of featurizations and feature transformations that allow for a complete representation of biomolecules like proteins and nucleic acids, including water and ion cavities within the biomolecular structure, thus avoiding bias that would come with manual selection of features. PENSA implements various methods to systematically compare the distributions of these features across ensembles to find the significant differences between them and identify regions of interest. It also includes a novel approach to quantify the state-specific information between two regions of a biomolecule which allows, e.g., the tracing of information flow to identify signaling pathways. PENSA is a modular open-source library that also comes with convenient tools for loading data and visualizing results in ways that make them quick to process and easy to interpret. This talk will demonstrate its usefulness in real-world examples by showing how it helps to determine molecular mechanisms efficiently.

Speaker: Martin Vögele

Twitter -  ⁠⁠⁠⁠⁠⁠⁠Prudencio⁠⁠⁠⁠⁠⁠⁠

Twitter - ⁠⁠⁠⁠⁠⁠⁠Jonny⁠⁠⁠⁠⁠⁠⁠

Twitter - ⁠⁠⁠⁠⁠⁠⁠datamol.io

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠⁠⁠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: ⁠⁠⁠https://datamol.io/⁠⁠⁠

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Abstract: Cryptic pockets, which are absent in ligand-free structures and have the potential to be used as drug targets, are often challenging to access through conventional biomolecular simulations due to their slow motions. To overcome this limitation, we have combined AlphaFold and Markov State modelling (MSM) to accelerate the discovery of cryptic pockets. AlphaFold was used to generate a diverse structural ensemble with open or partially open pockets that can serve as starting points for molecular dynamics simulations which were later stitched together using MSM to predict free energy and kinetics associated with cryptic pocket opening. Our approach explored known cryptic pockets, as well as discovered new cryptic pockets which were absent in PDB. Our study highlighted the power of AlphaFold and MSM to discover novel cryptic pockets which can unlock development of next-gen therapeutics.

Speaker: Stephan Thaler

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[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠⁠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: ⁠⁠https://datamol.io/⁠⁠

If you enjoyed this talk, consider joining the ⁠⁠⁠⁠⁠Molecular Modeling and Drug Discovery (M2D2) talks⁠⁠⁠⁠⁠ live.

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Abstract: Cryptic pockets, which are absent in ligand-free structures and have the potential to be used as drug targets, are often challenging to access through conventional biomolecular simulations due to their slow motions. To overcome this limitation, we have combined AlphaFold and Markov State modelling (MSM) to accelerate the discovery of cryptic pockets. AlphaFold was used to generate a diverse structural ensemble with open or partially open pockets that can serve as starting points for molecular dynamics simulations which were later stitched together using MSM to predict free energy and kinetics associated with cryptic pocket opening. Our approach explored known cryptic pockets, as well as discovered new cryptic pockets which were absent in PDB. Our study highlighted the power of AlphaFold and MSM to discover novel cryptic pockets which can unlock development of next-gen therapeutics.

Speaker: ⁠Soumendranath Bhakat 

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Twitter - ⁠⁠⁠⁠⁠Jonny⁠⁠⁠⁠⁠

Twitter - ⁠⁠⁠⁠⁠datamol.io

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠⁠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: ⁠⁠https://datamol.io/⁠⁠

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Abstract: The fundamental equations that govern Nature at atomistic scales are well understood in terms of quantum mechanics. Solving such equations is not possible apart from very simple systems, yet solutions to this problem represent one of the grand challenges for computational sciences as it would allow an understanding of all properties of molecular systems. We investigate this challenge by solving the sampling and accuracy problems of atomistic simulations using machine learning, physics, and GPUs. Machine learning potentials, as universal many-body function approximators, could deliver the next-generation modeling approach, blurring the boundary between quantum mechanics, molecular mechanics, and coarse-grained simulations into a cohesive methodology. In recent years, incredible progress has been made in transferable molecular representations which can learn effective potential functions. New methods for learning such potentials and even the energetics of the underlying physical systems are now available. However, there are still problems in extending the generalizability, lack of accurate datasets, and handling of charges and charged molecules, all within a speed bound which must be able to handle large systems like protein complexes. In this talk, I will discuss how to advance these scientific problems toward next-generation molecular simulations both in the context of biomolecular simulations (ACEMD/OpenMM) and more general machine learning frameworks (TorchMD, TorchMD-NET).

Speaker: Gianni De Fabritiis

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Twitter - ⁠⁠⁠⁠⁠Jonny⁠⁠⁠⁠⁠

Twitter - ⁠⁠⁠⁠⁠datamol.io

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠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: ⁠https://datamol.io/⁠

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Abstract: Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure. Existing approaches usually pretrain protein language models on a large number of unlabeled amino acid sequences and then finetune the models with some labeled data in downstream tasks. Despite the effectiveness of sequence-based approaches, the power of pretraining on known protein structures, which are available in smaller numbers only, has not been explored for protein property prediction, though protein structures are known to be determinants of protein function. In this paper, we propose to pretrain protein representations according to their 3D structures. We first present a simple yet effective encoder to learn the geometric features of a protein. We pretrain the protein graph encoder by leveraging multiview contrastive learning and different self-prediction tasks. Experimental results on both function prediction and fold classification tasks show that our proposed pretraining methods outperform or are on par with the state-of-the-art sequence-based methods, while using much less pretraining data.

Speaker: Zuobai Zhang

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Twitter - ⁠⁠⁠⁠Jonny⁠⁠⁠⁠

Twitter - ⁠⁠⁠⁠datamol.io

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠YouTube channel ⁠⁠⁠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: https://datamol.io/

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Abstract: While computational methods have become a mainstay in drug discovery programs, many calculations are too time-consuming to be applied to large datasets. Active learning (AL), a machine learning method used to direct a search iteratively, can enable the application of computationally expensive methods such as relative binding free energy (RBFE) calculations to sets containing thousands of molecules. Moreover, AL can also be applied to virtual screening, enabling the rapid processing of billions of molecules. This presentation will provide an overview of active learning and highlight some applications in drug discovery.

Speakes: Pat Walter & James Thompson

Twitter -  ⁠⁠⁠Prudencio⁠⁠⁠

Twitter - ⁠⁠⁠Therence⁠⁠⁠

Twitter - ⁠⁠⁠Jonny⁠⁠⁠

Twitter - ⁠⁠⁠datamol.io

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠YouTube channel ⁠⁠⁠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: https://datamol.io/

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Abstract: Developing novel bioactive molecules is time-consuming, costly and rarely successful. As a mitigation strategy, we utilize, for the first time, cellular morphology to directly guide the de novo design of small molecules. We trained a conditional generative adversarial network on a set of 30 000 compounds using their cell painting morphological profiles as conditioning. Our model was able to learn chemistry-morphology relationships and influence the generated chemical space according to the morphological profile. We provide evidence for the targeted generation of known agonists when conditioning on gene overexpression profiles, even though no information on biological targets was used during training. Based on a target-agnostic readout, our approach facilitates knowledge transfer between biological pathways and can be used to design bioactives for many targets under one unified framework. Prospective application of this proof-of-concept to larger chemical spaces promises great potential for hit generation in drug and phytopharmaceutical discovery and chemical safety.

Speaker: Paula A. Marin Zapata

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