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Abstract: The process of finding molecules that bind to a target protein is a challenging first step in drug discovery. Crystallographic fragment screening is a strategy based on elucidating binding modes of small polar compounds and then building potency by expanding or merging them. Recent advances in high-throughput crystallography enable screening of large fragment libraries, reading out dense ensembles of fragments spanning the binding site. However, fragments typically have low affinity thus the road to potency is often long and fraught with false starts. Here, we take advantage of high-throughput crystallography to reframe fragment-based hit discovery as a denoising problem – identifying significant pharmacophore distributions from a fragment ensemble amid noise due to weak binders – and employ an unsupervised machine learning method to tackle this problem. Our method screens potential molecules by evaluating whether they recapitulate those fragment-derived pharmacophore distributions. We retrospectively validated our approach on an open science campaign against SARS-CoV-2 main protease (Mpro), showing that our method can distinguish active compounds from inactive ones using only structural data of fragment-protein complexes, without any activity data. Further, we prospectively found novel hits for Mpro and the Mac1 domain of SARS-CoV-2 non-structural protein 3. More broadly, our results demonstrate how unsupervised machine learning helps interpret high throughput crystallography data to rapidly discover of potent chemical modulators of protein function.

Speaker: ⁠William McCorkindale ⁠

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Abstract: Deep learning models that leverage large datasets are often the state of the art for modelling molecular properties. When the datasets are smaller (less than 2000 molecules), it is not clear that deep learning approaches are the right modelling tool. In this work we perform an extensive study of the calibration and generalizability of probabilistic machine learning models on small chemical datasets. Using different molecular representations and models, we analyse the quality of their predictions and uncertainties in a variety of tasks (binary, regression) and datasets. We also introduce two simulated experiments that evaluate their performance: (1) Bayesian optimization guided molecular design, (2) inference on out-of-distribution data via ablated cluster splits. We offer practical insights into model and feature choice for modelling small chemical datasets, a common scenario in new chemical experiments. We have packaged our analysis into the DIONYSUS repository, which is open sourced to aid in reproducibility and extension to new datasets.

Speaker: ⁠Gary Tom⁠

Twitter -  ⁠Prudencio⁠

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Abstract: In computer-aided drug discovery, quantitative structure activity relation models are trained to predict biological activity from chemical structure. Despite the recent success of applying graph neural network to this task, important chemical information such as molecular chirality is ignored. To fill this crucial gap, we propose Molecular-Kernel Graph Neural Network (MolKGNN) for molecular representation learning, which features SE(3)-/conformation invariance, and interpretability. For our MolKGNN, we first design a molecular graph convolution to capture the chemical pattern by comparing the atom’s similarity with the learnable molecular kernels. Furthermore, we propagate the similarity score to capture the higher-order chemical pattern. To assess the method, we conduct a comprehensive evaluation with nine well-curated datasets spanning numerous important drug targets that feature realistic high class imbalance and it demonstrates the superiority of MolKGNN over other GNNs in CADD. Meanwhile, the learned kernels identify patterns that agree with domain knowledge, confirming the pragmatic interpretability of this approach. This work was recently accepted by AAAI23.

Speaker: Yunchao (Lance) Liu

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Abstract: Deep learning, and in general, auto-differentiation frameworks allow the expression of many scientific problems as end-to-end learning tasks. Common themes in scientific machine learning involve learning surrogate functions of expensive simulators, sampling complex distributions directly or time-propagation of known or unknown differential equation systems efficiently. We will describe our recent work in applying deep-learning surrogates and auto-differentiation techniques in molecular simulations. In particular, we will explore active learning of machine learning potentials with differentiable uncertainty; the use of deep neural network generative models to learn reversible coarse-grained representations of atomic systems; and the application of differentiable simulations for reaction path finding without prior knowledge of collective variables. Overfitting and lack of generalizability are constant challenges in AI for science. We will discuss the scalability of active learning to practical applications in molecular simulations and the sensitivity of deep learning answers to molecular problems, like the fitting of pair potentials from observables.

Speaker: Can Chen

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Abstract: Deep learning, and in general, auto-differentiation frameworks allow the expression of many scientific problems as end-to-end learning tasks. Common themes in scientific machine learning involve learning surrogate functions of expensive simulators, sampling complex distributions directly or time-propagation of known or unknown differential equation systems efficiently. We will describe our recent work in applying deep-learning surrogates and auto-differentiation techniques in molecular simulations. In particular, we will explore active learning of machine learning potentials with differentiable uncertainty; the use of deep neural network generative models to learn reversible coarse-grained representations of atomic systems; and the application of differentiable simulations for reaction path finding without prior knowledge of collective variables. Overfitting and lack of generalizability are constant challenges in AI for science. We will discuss the scalability of active learning to practical applications in molecular simulations and the sensitivity of deep learning answers to molecular problems, like the fitting of pair potentials from observables.

Speaker: Rafael Gomez-Bombarelli

Twitter -  Prudencio

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Abstract: Accurate 3D molecular information is a keystone for many computational programs but accessing reliable 3D conformers is still challenging. It requires enumerating and optimizing a huge isomer and conformer space, which would overwhelm any traditional computational methods. In light of this, we proposed the Auto3D package for generating low-energy 3D conformers using fast and reliable neural network potentials (NNPs). Given a SMILES, Auto3D returns the low-energy 3D conformers by automatizing the isomer enumeration and duplicate filtering process, 3D building process, geometry optimization process, and ranking process. In conjunction with Auto3D, we developed ANI-2xt NNP, which was trained especially for tautomer-related tasks. These NNPs were used to generate 3D structures and compute molecular properties. In a tautomeric reaction energy calculation task, the ANI-2xt NNP achieved similar accuracy but was several orders of magnitude faster than the reference DFT method.

Speaker: Zhen Liu

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Abstract: Recently, artificial intelligence (AI) for drug discovery has raised increasing interest in both the machine learning (ML) and computational chemistry communities. The core problem of AI for drug discovery is molecule representation learning, where the molecule knowledge can be naturally presented in different modalities: chemical formula, molecular graph, geometric conformation, knowledge base, biomedical literature, etc. In this talk, I would like to provide a perspective concentrating on molecule pretraining from topology, geometry, and textual description. Such a unified perspective paves the way for molecule representation interpretation as well as discovery tasks.

Speaker: Shengchao Liu

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Abstract: Molecular dynamics (MD) simulation techniques are widely used for various natural science applications. Increasingly, machine learning (ML) force field (FF) models begin to replace ab-initio simulations by predicting forces directly from atomic structures. Despite significant progress in this area, such techniques are primarily benchmarked by their force/energy prediction errors, even though the practical use case would be to produce realistic MD trajectories. We aim to fill this gap by introducing a novel benchmark suite for ML MD simulation. We curate representative MD systems, including water, organic molecules, peptide, and materials, and design evaluation metrics corresponding to the scientific objectives of respective systems. We benchmark a collection of state-of-the-art (SOTA) ML FF models and illustrate, in particular, how the commonly benchmarked force accuracy is not well aligned with relevant simulation metrics. We demonstrate when and how selected SOTA methods fail, along with offering directions for further improvement. Specifically, we identify stability as a key metric for ML models to improve. Our benchmark suite comes with a comprehensive open-source codebase for training and simulation with ML FFs to facilitate further work.

Speaker: Xiang Fu

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Abstract: The universality of thermodynamics and statistical mechanics has led to a language comprehensible to chemists, physicists & others, enabling countless scientific discoveries in diverse fields. In the last decade, a new arguably common language that everyone seems to speak but at least no chemist fully understands, has emerged with the advent of artificial intelligence (AI). It is natural to ask if AI can be integrated with the various theoretical and simulation methods in chemistry for new discoveries. At the same this raises many open questions, including: (1) should chemists, who are not fundamentally trained in AI, trust any of the results obtained using AI, (2) can AI paradigms developed for non-molecular systems with massive training data can directly be applied to chemistry with all its quirks, richness, known/unknown laws, and often poor/limited data? In this seminar I will show how such an integration of disciplines can be attained, creating trustable, robust AI frameworks for use by chemists. I will demonstrate such methods on different problems involving protein kinases, riboswitches and crystal polymorph nucleation, where we predict mechanisms at timescales much longer than milliseconds while keeping all-atom/femtosecond resolution. I will conclude with an outlook for future challenges and opportunities, envisioning a new sub-discipline of “Artificial Chemical Intelligence” where chemistry moves hand-in-hand with AI to enable smart molecular discovery, and is not just yet another domain for application of AI.

Speaker: Pratyush Tiwary

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Abstract: The latest biological findings observe that the motionless lock-and-key theory is no longer applicable and that changes in atomic sites and binding pose can provide important information for understanding drug binding. However, the computational expenditure limits the growth of protein trajectory-related studies, thus hindering the possibility of supervised learning. We present a novel spatial-temporal pre-training method based on the modified Equivariant Graph Matching Networks (EGMN), dubbed ProtMD. It has two specially designed self-supervised learning tasks: atom-level prompt-based denoising generative task and conformation-level snapshot ordering task to seize the flexibility information inside MD trajectories with very fine temporal resolutions. More importantly, we investigate the underlying mechanism behind the success of ProtMD, and further demonstrate a tight correlation between the magnitude of spatial motion of conformation and the extent to which the ligand and the receptor bind with each other.

Speaker: Fang Wu

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