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Abstract: The problem of molecular generation has received significant attention recently. Existing methods are typically based on deep neural networks and require training on large datasets with tens of thousands of samples. In practice, however, the size of class-specific chemical datasets is usually limited (e.g., dozens of samples) due to labor-intensive experimentation and data collection. This presents a considerable challenge for the deep learning generative models to comprehensively describe the molecular design space. Another major challenge is to generate only physically synthesizable molecules...

Speaker: Minghao Guo - https://twitter.com/GuoMh14

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Abstract: Finding synthesis routes for molecules of interest is essential in the discovery of new drugs and materials. To find such routes, computer-assisted synthesis planning (CASP) methods are employed, which rely on a single-step model of chemical reactivity. In this study, we introduce a template-based single-step retrosynthesis model based on Modern Hopfield Networks, which learn an encoding of both molecules and reaction templates in order to predict the relevance of templates for a given molecule...

Speaker: Philipp Seidl - https://twitter.com/phseidl

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Abstract: Proximity-inducing compounds (PICs) are an emergent drug technology through which the protein of interest (POI) is brought into the vicinity of proteins that control various cellular processes, giving rise to therapeutic benefits. One of the best-known PICs examples are heterobifunctional molecules known as proteolysis targeting chimeras (PROTACs), which induce protein degradation by establishing proximity between a POI and an E3 ligase. In silico PROTAC discovery requires computationally predicting the ternary complex consisting of POI, PROTAC molecule, and E3 ligase. To date, however, all of the approaches for modeling ternary complexes have not been both effective and computationally fast enough. We present a novel machine learning-based method for predicting PROTAC-mediated ternary complex structures based on Bayesian optimization. We show how a fitness combining an estimation of protein-protein interactions with PROTAC energy allows to find good candidate structures...

Speaker: Noah Weber

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Abstract: Molecular stereochemistry strongly alters (bio)chemical interactions yet is often neglected in molecular deep learning. Tetrahedral (point) chirality, a form of stereochemistry describing relative spatial arrangements of bonded neighbours around tetrahedral carbon centers, especially influences substrate-catalyst binding and is therefore critical for pharmaceutical drug design and asymmetric catalysis...

Speaker: Keir Adams - https://twitter.com/keiradams 

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Abstract: ML has become a crucial tool in drug discovery and chemistry at large, e.g. to predict molecular properties, such as bioactivity, with high levels of accuracy. However, activity cliffs – pairs of molecules that are highly similar in their structure but exhibit large differences in potency – have been underinvestigated for their effect on model performance. Not only are these edge cases informative for molecule discovery and optimization, but models that are well-equipped to accurately predict the potency of activity cliffs have an increased potential for prospective applications...

Speaker: Derek van Tilborg - https://twitter.com/DerekvTilborg 

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Abstract: In this talk, I will explain how to empower graph neural networks (GNNs) for molecular property prediction with more expressive models and large datasets. (GNNs) have emerged as one of the most important innovations for machine learning in drug discovery. Their ability to work on unstructured data enables us to use deep learning on molecular graphs, with the promise of predicting molecular properties with the same speed and accuracy that convolutional networks process images.

Speaker: Dominique Beaini - https://twitter.com/dom_beaini 

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Abstract: In computational drug discovery we often rely on surrogate models for molecular design and optimization. The estimates of the epistemic uncertainty of those surrogate models can be useful signals to enable efficient exploration in the molecular space. Traditional methods such as Bayesian optimization use Bayesian models like Gaussian Processes (GPs) as surrogates. Gaussian processes provide well calibrated uncertainty estimates, but do not scale trivially to large datasets and require hand-crafted kernels to work with structured data like strings (SMILES, peptides) and graphs...

Speaker: Moksh Jain - https://mj10.github.io/ 

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Abstract: Combining drugs opens new possibilities for tailoring therapies to a given disease and targeting several biological pathways at the same time. However the number of possible drug combinations is huge, and only a tiny portion of this space can be explored in a reasonable amount of time. One could either narrowly focus on experimenting with a very restricted number of well-chosen drugs, based on pre-existing biological knowledge, or broaden the scope by exploring uncharted territories with the risk of very low time and cost efficiency. 

Speaker: Paul Bertin - https://bertinus.github.io/ 

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Abstract: Understanding the 3D structures and interactions of proteins and drug-like molecules is a key part of therapeutics discovery. A core problem is molecular docking, i.e., determining how two molecules attach and create a molecular complex. Having access to very fast accurate computational docking tools would enable applications such as virtual screening of cancer protein inhibitors, de novo drug design, or rapid in silico drug side-effect prediction. In this talk, I will show that geometry and deep learning (DL) can significantly reduce this enormous search space inherent in docking and molecular conformation prediction. I will present EquiDock and EquiBind, our recent DL architectures for direct shot prediction of the molecular complex, and GeoMol, a model for 3D molecular flexibility. 

Speaker: Octavian-Eugen Ganea - https://twitter.com/octavianEganea

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Abstract: Machine learning methodologies have become increasingly established in many chemistry-related disciplines. Recent advances in quantum machine learning (QML) have enabled the prediction of QM-properties at a fraction of the cost of first-principle methods such as density functional theory (DFT). However, previous work has generally been focused on molecular systems of limited size and atom type diversity, hindering its application to adjacent fields such as drug discovery. The limited availability of open-source, high-performance models has further rendered the adoption of this progress to new fields challenging. We introduce two contributions towards overcoming these issues: The QMugs data collection (Quantum Mechanical properties of drug-like molecules) provides a wide array of QM-properties for large and biologically relevant molecules, increasing the chemical space accessible to ML models. 

Speaker: Clemens Isert - https://twitter.com/clemensisert 

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