ATOM3D: Tasks On Molecules in Three Dimensions

1 INTRODUCTION . A molecule ’ s three-dimensional ( 3D ) shape is critical to understanding its physical mechanisms of action , and can be used to answer a number of questions relating to drug discovery , molecular design , and fundamental biology . A molecule ’ s atoms often adopt specific 3D configurations that minimize its free energy , and by representing these 3D positions—the atomistic geometry—we can model this 3D shape in ways that would not be possible with 1D or 2D representations such as linear sequences or chemical bond graphs ( Table 1 ) . However , existing works that examine diverse molecular tasks , such as MoleculeNet ( Wu et al. , 2018 ) or TAPE ( Rao et al. , 2019 ) , focus on these lower dimensional representations . In this work , we demonstrate the benefit yielded by learning on 3D atomistic geometry and promote the development of 3D molecular learning by providing a collection of datasets leveraging this representation . Furthermore , we argue that the atom should be considered a “ machine learning datatype ” in its own right , deserving focused study much like images in computer vision or text in natural language processing . All molecules , including proteins , small molecule compounds , and nucleic acids , can be homogeneously represented as atoms in 3D space . These atoms can only belong to a fixed class of element types ( e.g . carbon , nitrogen , oxygen ) , and are all governed by the same underlying laws of physics , leading to important rotational , translational , and permutational symmetries . These systems also contain higher-level patterns that are poorly characterized , creating a ripe opportunity for learning them from data : though certain basic components are well understood ( e.g . amino acids , nucleic acids , functional groups ) , many others can not easily be defined . These patterns are in turn composed in a hierarchy that itself is only partially elucidated . While deep learning methods such as graph neural networks ( GNNs ) and convolutional neural networks ( CNNs ) seem especially well suited to atomistic geometry , to date there has been no systematic evaluation of such methods on molecular tasks . Additionally , despite the growing number of 3D structures available in databases such as the Protein Data Bank ( PDB ) ( Berman et al. , 2000 ) , they require significant processing before they are useful for machine learning tasks . Inspired by the success of accessible databases such as ImageNet ( Jia Deng et al. , 2009 ) and SQuAD ( Rajpurkar et al. , 2016 ) in sparking progress in their respective fields , we create and curate benchmark datasets for atomistic tasks , process them into a simple and standardized format , systematically benchmark 3D molecular learning methods , and present a set of best practices for other machine learning researchers interested in entering the field of 3D molecular learning . We develop new methods for several datasets and reveal a number of insights related to 3D molecular learning , including the consistent improvements yielded by using atomistic geometry , the lack of a single dominant method , and the presence of several tasks that can be improved through 3D molecular learning . 2 RELATED WORK . Three dimensional molecular data have long been pursued as an attractive source of information in molecular learning and chemoinformatics , but until recently have achieved underwhelming results relative to 1D and 2D representations ( Swamidass et al. , 2005 ; Azencott et al. , 2007 ) . However , due to increases in data availability and methodological advances , machine learning methods based on 3D molecular structure have begun to demonstrate significant impact in the last couple of years on specific tasks such as protein structure prediction ( Senior et al. , 2020 ) , equilibrium state sampling ( Noé et al. , 2019 ) , and drug design ( Zhavoronkov et al. , 2019 ) . While there have been some broader assessments of groups of related biological tasks , these have focused on on either 1D ( Rao et al. , 2019 ) or 2D ( Wu et al. , 2018 ) representations . By focusing instead on atomistic geometry , we can consistently improve performance and address disparate problems involving any combination of small molecules , proteins , and nucleic acids through a unified lens . Graph neural networks ( GNNs ) have grown to be a major area of study , providing a natural way of learning from data with complex spatial structure . Many GNN implementations have been motivated by applications to atomic systems , including molecular fingerprinting ( Duvenaud et al. , 2015 ) , property prediction ( Schütt et al. , 2017 ; Gilmer et al. , 2017 ; Liu et al. , 2019 ) , protein interface prediction ( Fout et al. , 2017 ) , and protein design ( Ingraham et al. , 2019 ) . Instead of encoding points in Euclidean space , GNNs encode their pairwise connectivity , capturing a structured representation of atomistic data . Three-dimensional CNNs ( 3DCNNs ) have also become popular as a way to capture these complex 3D geometries . They have been applied to a number of biomolecular applications such as protein interface prediction ( Townshend et al. , 2019 ) , protein model quality assessment ( Pagès et al. , 2019 ; Derevyanko et al. , 2018 ) , protein sequence design ( Anand et al. , 2020 ) , and structure-based drug discovery ( Wallach et al. , 2015 ; Torng & Altman , 2017 ; Ragoza et al. , 2017 ; Jiménez et al. , 2018 ) . These 3DCNNs can encode translational and permutational symmetries , but incur significant computational expense and can not capture rotational symmetries without data augmentation . In an attempt to address many of the problems of representing atomistic geometries , equivariant neural networks ( ENNs ) have emerged as a new class of methods for learning from molecular systems . These networks are built such that geometric transformations of their inputs lead to well-defined transformations of their outputs . This setup leads to the neurons of the network learning rules that resemble physical interactions . Tensor field networks ( Thomas et al. , 2018 ) and Cormorant ( Kondor , 2018 ; Anderson et al. , 2019 ) have applied these principles to atomic systems and begun to demonstrate promise on extended systems ( Eismann et al. , 2020 ; Weiler et al. , 2018 ) . However , in general , these methods have not been applied to larger-scale molecular tasks . 3 3D MOLECULAR LEARNING . We define 3D molecular learning as the set of tasks where the input space is atoms in three dimensions . We write this space as AN where A = P × E. P = R3 is the position space and E = { C , H , O , N , P , S , ... } is the element space . We select 3D molecular learning tasks from structural biophysics and medicinal chemistry that span a variety of molecule types and address a range of important problems . Multiple of these datasets are novel , while others are extracted from existing sources ( Table 2 ) . We provide all datasets in a standardized format that requires no specialized libraries . Alongside these datasets , we present corresponding best practices , including splitting and filtering criteria , to minimize data leakage concerns and ensure generalizability and reproducibility . Taken together , we hope these efforts will lower the barrier to entry for machine learning researchers interested in developing methods for 3D molecular learning and encourage rapid progress in the field . Detailed descriptions of the preparation of each dataset can be found in Appendix C.1 . 3.1 SMALL MOLECULE PROPERTIES ( SMP ) . Impact – Predicting physico-chemical properties of small molecules is a common task in medicinal chemistry and materials design . Quantum chemical calculations can save expensive experiments but are themselves costly and can not cover the huge chemical space spanned by candidate molecules . Dataset – The QM9 dataset ( Ruddigkeit et al. , 2012 ; Ramakrishnan et al. , 2014 ) contains structures and energetic , electronic , and thermodynamic properties for 134,000 stable small organic molecules , obtained from quantum-chemical calculations . Metrics – We predict the molecular properties from the ground-state structure . Split – We split molecules randomly . 3.2 PROTEIN INTERFACE PREDICTION ( PIP ) . Impact – Proteins interact with each other in many scenarios—for example , antibody proteins recognize diseases by binding to antigens . A critical problem in understanding these interactions is to identify which amino acids of two given proteins will interact upon binding . Dataset – For training , we use the Database of Interacting Protein Structures ( DIPS ) , a comprehensive dataset of protein complexes mined from the PDB ( Townshend et al. , 2019 ) . We predict on the Docking Benchmark 5 ( Vreven et al. , 2015 ) , a smaller gold standard dataset . Metrics – We predict if two amino acids will come into contact when their respective proteins bind . Split – We split protein complexes by sequence identity at 30 % . 3.3 RESIDUE IDENTITY ( RES ) . Impact – Understanding the structural role of individual amino acids is important for engineering new proteins . We can understand this role by predicting the propensity for different amino acids at a given protein site based on the surrounding structural environment ( Torng & Altman , 2017 ) . Dataset – We generate a novel dataset consisting of atomic environments extracted from nonredundant structures in the PDB . Metrics – We formulate this as a classification task where we predict the identity of the amino acid in the center of the environment based on all other atoms . Split – We split residue environments by protein topology class . 3.4 MUTATION STABILITY PREDICTION ( MSP ) . Impact – Identifying mutations that stabilize a protein ’ s interactions is a key task in designing new proteins . Experimental techniques for probing these are labor-intensive ( Antikainen & Martin , 2005 ; Lefèvre et al. , 1997 ) , motivating the development of efficient computational methods . Dataset – We derive a novel dataset by collecting single-point mutations from the SKEMPI database ( Jankauskaitė et al. , 2019 ) and model each mutation into the structure to produce mutated structures . Metrics – We formulate this as a binary classification task where we predict whether the stability of the complex increases as a result of the mutation . Split – We split protein complexes by sequence identity at 30 % . 3.5 LIGAND BINDING AFFINITY ( LBA ) . Impact – Most therapeutic drugs and many molecules critical for biological signaling take the form of small molecules . Predicting the strength of the protein-small molecule interaction is a challenging but crucial task for drug discovery applications . Dataset – We use the PDBBind database ( Wang et al. , 2004 ; Liu et al. , 2015 ) , a curated database containing protein-ligand complexes from the PDB and their corresponding binding strengths . Metrics – We predict pK = − log ( K ) , where K is the binding affinity in Molar units . Split – We split protein-ligand complexes by protein sequence identity at 30 % . 3.6 LIGAND EFFICACY PREDICTION ( LEP ) . Impact – Many proteins switch on or off their function by changing shape . Predicting which shape a drug will favor is thus an important task in drug design . Dataset – We develop a novel dataset by curating proteins from several families with both ” active ” and ” inactive ” state structures , and model in 527 small molecules with known activating or inactivating function using the program Glide ( Friesner et al. , 2004 ) . Metrics – We formulate this as a binary classification task where we predict whether or not a molecule bound to the structures will be an activator of the protein ’ s function or not . Split – We split complex pairs by protein .

Summary: In this paper, the authors introduce a repository of datasets for several atomistic learning tasks. These datasets are processed into a simple and standardized format. A systematic benchmark with atomistic learning methods is presented, showcasing the value of using 3D atom-level data instead of 1D or 2D features. The authors argue that these datasets will serve as a stepping stone for machine learning researchers interested in developing methods for atomistic learning and rapidly advance this field. The paper also presents the best practices for each of the tasks, as well as the splitting and filtering criteria to ensure generalizability and reproducibility.