GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
Department of Computer Science, Peking University（北京大学计算机科学系）；Shanghai Jiao Tong University（上海交通大学）；Mila - Québec AI Institute（Mila - 魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；HEC Montréal（蒙特利尔高等商学院）；CIFAR AI Research Chair（CIFAR人工智能研究主席（注：该机构名称为职位头衔，非机构））
Molecular graph generation is a fundamental problem for drug discovery and has been attracting growing attention. The problem is challenging since it requires not only generating chemically valid molecular structures but also optimizing their chemical properties in the meantime. Inspired by the recent progress in deep generative models, in this paper we propose a flow-based autoregressive model for graph generation called GraphAF. GraphAF combines the advantages of both autoregressive and flow-based approaches and enjoys: (1) high model flexibility for data density estimation; (2) efficient parallel computation for training; (3) an iterative sampling process, which allows leveraging chemical domain knowledge for valency checking. Experimental results show that GraphAF is able to generate 68% chemically valid molecules even without chemical knowledge rules and 100% valid molecules with chemical rules. The training process of GraphAF is two times faster than the existing state-of-the-art approach GCPN. After fine-tuning the model for goal-directed property optimization with reinforcement learning, GraphAF achieves state-of-the-art performance on both chemical property optimization and constrained property optimization.
NEAT: Neighborhood-Guided, Efficient, Autoregressive Set Transformer for 3D Molecular Generation
Department of Pharmaceutical Sciences, University of Vienna（维也纳大学药学系）；Christian Doppler Laboratory for Molecular Informatics in the Biosciences（生物医学分子信息学克里斯蒂安·多普勒实验室）；Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences（维也纳药学、营养学和运动科学博士学院）
Teaching Molecular Dynamics to a Non-Autoregressive Ionic Transport Predictor
School of Mathematics and Computing (Computational Science and Engineering)（数学与计算学院（计算科学与工程））；Yonsei University（延世大学）；Korea Institute of Science and Technology（韩国科学技术院）；Nanoscience and Technology（纳米科学与技术）
Graph generation techniques are increasingly being adopted for drug discovery. Previous graph generation approaches have utilized relatively small molecular building blocks such as atoms or simple cycles, limiting their effectiveness to smaller molecules. Indeed, as we demonstrate, their performance degrades significantly for larger molecules. In this paper, we propose a new hierarchical graph encoder-decoder that employs significantly larger and more flexible graph motifs as basic building blocks. Our encoder produces a multi-resolution representation for each molecule in a fine-to-coarse fas…
With the recent advances in machine learning for quantum chemistry, it is now possible to predict the chemical properties of compounds and to generate novel molecules. Existing generative models mostly use a string- or graph-based representation, but the precise three-dimensional coordinates of the atoms are usually not encoded. First attempts in this direction have been proposed, where autoregressive or GAN-based models generate atom coordinates. Those either lack a latent space in the autoregressive setting, such that a smooth exploration of the compound space is not possible, or cannot gene…
The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties. Our work in this paper substantially extends prior state-of-the-art on graph-to-graph translation methods for molecular optimization. In particular, we realize coherent multi-resolution representations by interweaving the encoding of substructure components with the atom-level encoding of the original molecular graph. Moreover, our graph decoder is fully autoregressive, and interleaves each step of adding a new substructure with the pro…
We propose a framework using normalizing-flow based models, SELF-Referencing Embedded Strings, and multi-objective optimization that efficiently generates small molecules. With an initial training set of only 100 small molecules, FastFlows generates thousands of chemically valid molecules in seconds. Because of the efficient sampling, substructure filters can be applied as desired to eliminate compounds with unreasonable moieties. Using easily computable and learned metrics for druglikeness, synthetic accessibility, and synthetic complexity, we perform a multi-objective optimization to demonst…
Department of Chemistry, University of Toronto, Toronto, ON, Canada；Department of Computer Science, University of Toronto, Toronto, ON, Canada；Vector Institute for Artificial Intelligence, Toronto, ON, Canada；Department of Chemistry；Chemical Biology, Rutgers University, Piscataway, NJ, USA；Department of Materials Science \& Engineering, University of Toronto, Toronto, ON, Canada；Department of Chemical Engineering \& Applied Chemistry, University of Toronto, Toronto, ON, Canada；Senior Fellow, Canadian Institute for Advanced Research (CIFAR), Toronto, ON, Canada；NVIDIA
SIGMA: Semantic Identifier Grouping for Molecular Autoregression
School of Computing, University of Connecticut（康涅狄格大学计算学院）；Institute for Artificial Intelligence, University of Georgia（佐治亚大学人工智能研究所）；Institute of Hydrobiology, Chinese Academy of Sciences（中国科学院水生生物研究所）
Unified Cross-Scale 3D Generation and Understanding via Autoregressive Modeling
DP Technology（DP技术公司）；AI for Science Institute（人工智能科学研究院）；School of Mathematical Sciences（数学科学学院）；Center for Machine Learning Research（机器学习研究中心）；Institute for Artificial Intelligence（人工智能研究院）
Department of Energy Conversion and Storage, Technical University of Denmark（能源转换与存储系，技术大学丹麦分校）；Department of Applied Mathematics and Computer Science, Technical University of Denmark（应用数学与计算机科学系，技术大学丹麦分校）
From Tokens to Blocks: A Block-Diffusion Perspective on Molecular Generation
School of Artificial Intelligence, Shenzhen University, Shenzhen 518060, China（人工智能学院，深圳大学，深圳518060，中国）；School of Computer Science, University of Nottingham Ningbo, Ningbo 315100, China（计算机科学学院，诺丁汉大学宁波分校，宁波315100，中国）
Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several drawbacks including lack of modeling important molecular geometry elements (e.g. torsion angles), separate optimization stages prone to error accumulation, and the need for structure fine-tuning based on approximate classical force-fields or computationally expensive methods such as metadynamics with approximate quantum mechanics calculations at each geometry. We propose GeoMol--an end-to-end, non-autoregressive and S…
De novo molecular generation with optical property preconditioning at the token level
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Universidad Autónoma de Madrid（马德里自治大学）；Canadian Institute for Advanced Research (CIFAR)（加拿大高等研究院）；NVIDIA（英伟达）
Shape-based virtual screening is widely employed in ligand-based drug design to search chemical libraries for molecules with similar 3D shapes yet novel 2D chemical structures compared to known ligands. 3D deep generative models have the potential to automate this exploration of shape-conditioned 3D chemical space; however, no existing models can reliably generate valid drug-like molecules in conformations that adopt a specific shape such as a known binding pose. We introduce a new multimodal 3D generative model that enables shape-conditioned 3D molecular design by equivariantly encoding molec…
SynCoGen: Synthesizable 3D Molecule Generation via Joint Reaction and Coordinate Modeling
University of Toronto（多伦多大学）；The Hospital for Sick Children（多伦多儿童医院）；University of Cambridge（剑桥大学）；ETH Zürich（苏黎世联邦理工学院）；Vector Institute（向量研究所）；Mila – Quebec AI Institute（魁北克AI研究所）；McGill University（麦吉尔大学）；Caltech（加州理工学院）
F$^3$low: Frame-to-Frame Coarse-grained Molecular Dynamics with SE(3) Guided Flow Matching
Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Microsoft Research AI4Science（微软研究院AI4科学部门）；Xi’an Jiaotong University（西安交通大学）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；HEC Montréal（蒙特利尔高等商学院）；Canadian Institute for Advanced Research (CIFAR)（加拿大高级研究院）
