Molecular Design

Molecular design leverages computational methods to create molecules with desired properties, accelerating drug discovery and materials science. Current research heavily employs machine learning, particularly generative models like variational autoencoders (VAEs), transformers, and GFlowNets, often integrated with reinforcement learning and Bayesian optimization to navigate the vast chemical space and ensure synthetic feasibility. These advancements enable efficient exploration of synthesizable molecules, optimization of multiple properties simultaneously, and the incorporation of diverse data types, including text descriptions and high-content imaging data, to guide the design process. The resulting improvements in efficiency and accuracy have significant implications for various scientific fields and industrial applications.

Papers

October 17, 2022

A Transformer-based Generative Model for De Novo Molecular Design
Wenlu Wang, Ye Wang, Honggang Zhao, Simone Sciabola
Molecular Design Drug Like Drug Target Transformer Based Generative De Novo

October 15, 2022

Substructure-Atom Cross Attention for Molecular Representation Learning
Jiye Kim, Seungbeom Lee, Dongwoo Kim, Sungsoo Ahn, Jaesik Park
Cross Attention Drug Discovery Molecular Representation Molecular Property Prediction Molecular Design

October 6, 2022

Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design
Keir Adams, Connor W. Coley
Molecular Design Virtual Screening Equivariant Map 3D Molecule Shape Generation Molecular Shape Ligand Based

September 30, 2022

Equivariant Energy-Guided SDE for Inverse Molecular Design
Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li, Chongxuan Li, Jun Zhu
Stochastic Differential Equation Molecular Design Equivariant Map Diffusion SDE 3D Molecule Generation Energy Function Inverse Molecular Design

September 20, 2022

Probabilistic Generative Transformer Language models for Generative Design of Molecules
Lai Wei, Nihang Fu, Yuqi Song, Qian Wang, Jianjun Hu
Known Molecule Molecular Design Generative Design Molecular Generative Molecule Text

September 13, 2022

Molecular Design Based on Integer Programming and Quadratic Descriptors in a Two-layered Model
Jianshen Zhu, Naveed Ahmed Azam, Shengjuan Cao, Ryota Ido, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu
Molecular Graph Molecular Design Integer Programming Molecular Structure Chemical Structure Chemical Graph

August 30, 2022

Tailoring Molecules for Protein Pockets: a Transformer-based Generative Solution for Structured-based Drug Design
Kehan Wu, Yingce Xia, Yang Fan, Pan Deng, Haiguang Liu, Lijun Wu, Shufang Xie, Tong Wang, Tao Qin, Tie-Yan Liu
Molecular Design Structure Based Drug Design De Novo Drug Design Computer Aided Drug Discovery Transformer Based Generative Drug Candidate Protein Pocket

August 14, 2022

Widely Used and Fast De Novo Drug Design by a Protein Sequence-Based Reinforcement Learning Model
Yaqin Li, Lingli Li, Yongjin Xu, Yi Yu
Reinforcement Learning Drug Discovery Molecular Design Molecular Generation Drug Design Protein Data De Novo Drug Design De Novo Protein Design

June 1, 2022

Graph Machine Learning for Design of High-Octane Fuels
Jan G. Rittig, Martin Ritzert, Artur M. Schweidtmann, Stefanie Winkler, Jana M. Weber, Philipp Morsch, K. Alexander Heufer, Martin Grohe, Alexander Mitsos, Manuel Dahmen
Product Design Molecular Design Graph Machine Learning Graph Generative Model Fuel Information

May 27, 2022

Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design
Jörg K. H. Franke, Frederic Runge, Frank Hutter
Molecular Design Hierarchical Latent miRNA Description Probabilistic Transformer Ambiguous Data RNA Folding

May 21, 2022

De novo design of protein target specific scaffold-based Inhibitors via Reinforcement Learning
Andrew D. McNaughton, Mridula S. Bontha, Carter R. Knutson, Jenna A. Pope, Neeraj Kumar
Reinforcement Learning Drug Discovery Molecular Design Protein Target De Novo Molecular Scaffold

May 15, 2022

3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design
Yinan Huang, Xingang Peng, Jianzhu Ma, Muhan Zhang
Molecular Graph Conditional Generative Molecular Design Equivariant Neural Equivariant Graph

April 22, 2022

3D pride without 2D prejudice: Bias-controlled multi-level generative models for structure-based ligand design
Lucian Chan, Rajendra Kumar, Marcel Verdonk, Carl Poelking
Generative Model Molecular Design Generative Process Hierarchical Generative Ligand Based

April 5, 2022

Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge
Mohammad Sajjad Ghaemi, Karl Grantham, Isaac Tamblyn, Yifeng Li, Hsu Kiang Ooi
Constructive Approach Molecular Design Sequential Learning Novel Molecule Molecular Fingerprint Chemical Discovery

March 28, 2022

MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design
Yuanqi Du, Tianfan Fu, Jimeng Sun, Shengchao Liu
Machine Learning Model Comprehensive Survey Known Molecule Molecular Design Research Survey Candidate Molecule

March 20, 2022

A 3D Generative Model for Structure-Based Drug Design
Shitong Luo, Jiaqi Guan, Jianzhu Ma, Jian Peng
3D Generative Molecular Design Structure Based Drug Design 3D Molecule 3D Protein

March 15, 2022

Data-Efficient Graph Grammar Learning for Molecular Generation
Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, Wojciech Matusik
Molecular Design Molecular Generation Graph Grammar

March 2, 2022

Biological Sequence Design with GFlowNets
Moksh Jain, Emmanuel Bengio, Alex-Hernandez Garcia, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ekbote, Jie Fu, Tianyu Zhang, Micheal Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio
Molecular Design Energy Based GFlowNets Biological Sequence Active Learning Algorithm Sequence Design

March 1, 2022

Molecular Design

Papers

A Transformer-based Generative Model for De Novo Molecular Design

Substructure-Atom Cross Attention for Molecular Representation Learning

Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design

Equivariant Energy-Guided SDE for Inverse Molecular Design

Probabilistic Generative Transformer Language models for Generative Design of Molecules

Molecular Design Based on Integer Programming and Quadratic Descriptors in a Two-layered Model

Tailoring Molecules for Protein Pockets: a Transformer-based Generative Solution for Structured-based Drug Design

Widely Used and Fast De Novo Drug Design by a Protein Sequence-Based Reinforcement Learning Model

Graph Machine Learning for Design of High-Octane Fuels

Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design

De novo design of protein target specific scaffold-based Inhibitors via Reinforcement Learning

3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design

3D pride without 2D prejudice: Bias-controlled multi-level generative models for structure-based ligand design

Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge

MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design

A 3D Generative Model for Structure-Based Drug Design

Data-Efficient Graph Grammar Learning for Molecular Generation

Biological Sequence Design with GFlowNets

Multi-Objective Latent Space Optimization of Generative Molecular Design Models

Bayesian Sequential Stacking Algorithm for Concurrently Designing Molecules and Synthetic Reaction Networks