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Abstract: Antibodies are versatile proteins that bind to pathogens like viruses and stimulate the adaptive immune system. The antibody binding affinity is determined by complementarity-determining regions (CDRs) at the tips of these Y-shaped proteins, which closely interact with antigen residues (epitopes). In this talk, I will present new generative models to automatically design the CDRs of antibodies with desired binding affinity. Specifically, our model seeks to co-design the sequence and 3D structure of CDRs as graphs. It unravels a sequence auto regressively while iteratively refining its predicted global 3D structure. Our model is evaluated on binder design tasks and shows superior performance compared to existing baselines.

Speaker: Wengong Jin - http://people.csail.mit.edu/wengong/ 

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Abstract: Beyond the active search for new drugs, de novo generation methods are also a great opportunity for the discovery of molecular materials. However, the chemical space of these materials differs from that of bioactive molecules. This conference will present the challenges inherent to this kind of problems. For most molecular materials, new targets must have specific electronic properties. This normally means a very costly evaluation by quantum mechanical calculations. Furthermore this evaluation requires a knowledge of the atomic positions in three dimensions. All these specific constraints have led us to propose our own generation method based on EvoMol, an efficient evolutionary algorithm. Free to travel the whole chemical space, the methods that limit the solutions to realistic molecules will be presented.

Speaker: Thomas Cauchy - https://twitter.com/ThomasCauchyQC 

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Abstract: In organic chemistry, we are currently witnessing a rise in artificial intelligence (AI) approaches, which show great potential for improving molecular designs, facilitating synthesis and accelerating the discovery of novel molecules. Based on an analogy between written language and organic chemistry, we built linguistics-inspired transformer neural network models for chemical reaction prediction, synthesis planning, and the prediction of experimental actions. We extended the models to chemical reaction classification and fingerprints. By finding a mapping from discrete reactions to continuous vectors, we enabled efficient chemical reaction space exploration. Moreover, we specialized similar models for reaction yield predictions. Intrigued by the remarkable performance of chemical language models, we discovered that the models can capture how atoms rearrange during a reaction, without supervision or human labelling, leading to the development of the open-source atom-mapping tool RXNMapper. During my talk, I will provide an overview of the different contributions that are at the base of this digital synthetic chemistry revolution. 

Speaker: Philippe Schwaller - https://twitter.com/pschwllr 

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Abstract: Deep learning in molecular and materials sciences is limited by the lack of integration between applied science, artificial intelligence, and high-performance computing. Bottlenecks with respect to the amount of training data, the size and complexity of model architectures, and the scale of compute infrastructure are all key factors limiting the scaling of deep learning for molecules and materials. In cases where design goals require explorations of vast areas of chemical/material space, or target properties are prohibitively expensive to compute, efficient use of resources and careful choice of method enable new capabilities for design. We explore interactive supercomputing for applying high-throughput virtual screening and machine learning to challenges in materials and chemistry. The abundance of data from first-principles calculations introduces a need to identify and investigate scalable neural network architectures that operate on graphs, which are a natural representation for atomistic systems. We present LitMatter, a lightweight framework for scaling geometric deep learning methods. We discuss scaling atomistic deep learning using key resources including compute, model and dataset sizes, and energy. We train four graph neural network architectures on over 400 GPUs and investigate the scaling behavior of these methods. Depending on the model architecture, training time speedups up to 60x are seen. Empirical neural scaling relations quantify the model-dependent scaling and enable optimal compute resource allocation and the identification of scalable geometric deep learning model implementations. Training speed estimation and energy monitoring are used to accelerate hyperparameter optimization for neural interatomic potentials, and quantify the efficiency of physics-informed architectures. We discuss applications of scalable ML to property prediction tasks, deep generative modeling, and neural force fields for fully differentiable simulations.

Speaker: Nathan C. Frey - https://ncfrey.github.io/

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Abstract: Scientists and engineers in diverse domains need to perform expensive experiments to optimize combinatorial spaces, where each candidate input is a discrete structure (e.g., sequence, tree, graph) or a hybrid structure (mixture of discrete and continuous design variables). For example, in drug and vaccine design, we need to search a large space of molecules guided by physical lab experiments. These experiments are often performed in a heuristic manner by humans and without any formal reasoning. Bayesian optimization (BO) is an efficient framework for optimizing expensive black-box functions. However, most of the BO literature is largely focused on optimizing continuous spaces. In this talk, I will discuss the main challenges in extending BO framework to combinatorial structures and some algorithms that I have developed in addressing them.

Speaker: Aryan Deshwal - https://aryandeshwal.github.io/  

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Abstract: Meta-learning transfers knowledge across tasks and domains to learn new tasks efficiently, which has shown promise in drug discovery. However, the generalization ability of current meta-learning methods is limited by task heterogeneity and memorization. In this talk, I will first introduce two general principles to improve the generalization ability in meta-learning: organization and augmentation. Then, I will present several concrete few-shot drug discovery instantiations of using each principle. This includes algorithms to organize and adapt knowledge and a simple method for sufficiently overcoming task memorization. The remaining challenges and promising future research directions will also be discussed.

Speaker:  Huaxiu Yao - https://huaxiuyao.mystrikingly.com/  

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Abstract: One of the challenges with deep learning is lack of model interpretability. This is a significant drawback in the chemistry domain as lack of knowledge why a certain prediction was made dissuades chemists to trust predictions from deep learning. In this work we propose a method that can provide local explanations for arbitrary models with the use of molecular counterfactuals. These are sparse explanations composed of molecular structures. A counterfactual is an example as close to the original, but with a different outcome. Although relatively new to AI, counterfactual explanations are a mature topic in philosophy and mathematics. We use counterfactuals to answer, “what is the smallest change to the features that would alter the prediction". Our Molecular Model Agnostic Counterfactual Explanations (MMACE), method is built on the STONED (Nigam et al., 2021) algorithm to traverse a local chemical space around a given base molecule to identify counterfactuals. Further, we introduce an open-source software named “exmol” that implements the MMACE algorithm for generating counterfactual explanations.  

Speaker:  Geemi Wellawatte - https://geemi725.github.io/  

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Abstract: Molecular design and synthesis planning are two critical steps in the process of molecular discovery that we propose to formulate as a single shared task of conditional synthetic pathway generation. We report an amortized approach to generate synthetic pathways as a Markov decision process conditioned on a target molecular embedding. This approach allows us to conduct synthesis planning in a bottom-up manner and design synthesizable molecules by decoding from optimized conditional codes, demonstrating the potential to solve both problems of design and synthesis simultaneously. The approach leverages neural networks to probabilistically model the synthetic trees, one reaction step at a time, according to reactivity rules encoded in a discrete action space of reaction templates. We train these networks on hundreds of thousands of artificial pathways generated from a pool of purchasable compounds and a list of expert-curated templates. We validate our method with (a) the recovery of molecules using conditional generation, (b) the identification of synthesizable structural analogs, and (c) the optimization of molecular structures given oracle functions relevant to drug discovery.

Speaker: Wenhao Gao - https://twitter.com/wenhaogao1

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Abstract: Machine learning for therapeutics offer incredible opportunities for expansion, innovation, and impact. Despite promises, many challenges exist. In this talk, the speaker will first highlight two high-impact but relatively understudied directions - ML-aided clinical trial design and low-data/cross-context biomedicine. Then, he will discuss challenges arising from therapeutics ML adoption in the wild, namely, generating actionable hypotheses and user interface with domain scientists. Lastly, challenges in infrastructure, such as data and benchmark, will be looked at. 

Speaker: Kexin Huang - https://www.kexinhuang.com/  

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Abstract: Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts. Including 3D molecular structure as input to learned models improves their performance for many molecular tasks. However, this information is infeasible to compute at the scale required by several real-world applications. We propose pre-training a model to reason about the geometry of molecules given only their 2D molecular graphs. Using methods from self-supervised learning, we maximize the mutual information between 3D summary vectors and the representations of a Graph Neural Network (GNN) such that they contain latent 3D information. During fine-tuning on molecules with unknown geometry, the GNN still generates implicit 3D information and can use it to improve downstream tasks. We show that 3D pre-training provides significant improvements for a wide range of properties, such as a 22% average MAE reduction on eight quantum mechanical properties. Moreover, the learned representations can be effectively transferred between datasets in different molecular spaces.  

Speaker: Hannes Stärk - https://hannes-stark.com/

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