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RESEARCH PAPER · Repository KITopen (Karlsruhe Institute of Technology) · 2020

Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation

Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru‐Guzik

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Self-referencing embedded strings (SELFIES): A 100% robust molecular string representationOriginalConstrained Bayesian Optimization for Automatic Chemical Design45On failure modes in molecule generation and optimization154Generative Models for Automatic Chemical Design88Exploring chemical space using natural language processing methodologies for drug discovery160Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning50VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder44Applications of machine learning in metal-organic frameworks225Graph Neural Networks for Prediction of Fuel Ignition Quality139Designing and understanding light-harvesting devices with machine learning112EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation75OptiMol : Optimization of Binding Affinities in Chemical Space for Drug Discovery67ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction379DECIMER: towards deep learning for chemical image recognition96Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently55Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders119Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models828Digitizing Chemistry Using the Chemical Processing Unit: From Synthesis to Discovery94Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES165Data-driven algorithms for inverse design of polymers100Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases62Inverse design of nanoporous crystalline reticular materials with deep generative models400Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning88Data-Driven Strategies for Accelerated Materials Design473SMILES Pair Encoding: A Data-Driven Substructure Tokenization Algorithm for Deep Learning133Deep Learning Optical Spectroscopy Based on Experimental Database: Potential Applications to Molecular Design170Automation and computer-assisted planning for chemical synthesis189STOUT: SMILES to IUPAC names using neural machine translation59Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study76Machine learning directed drug formulation development335Masked graph modeling for molecule generation93Materials representation and transfer learning for multi-property prediction71Adsorption Enthalpies for Catalysis Modeling through Machine-Learned Descriptors104Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations45Assigning confidence to molecular property prediction66Chemical language models enable navigation in sparsely populated chemical space167Machine learning based energy-free structure predictions of molecules, transition states, and solids98Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats323Ab Initio Machine Learning in Chemical Compound Space195A Gentle Introduction to Graph Neural Networks234DECIMER 1.0: deep learning for chemical image recognition using transformers74Chemputation and the Standardization of Chemical Informatics73Applied Machine Learning for Developing Next‐Generation Functional Materials72Generative Deep Learning for Targeted Compound Design191Predicting Antimicrobial Activity of Conjugated Oligoelectrolyte Molecules via Machine Learning57Artificial Intelligence for Autonomous Molecular Design: A Perspective49Machine learning for polymeric materials: an introduction95Geometric deep learning on molecular representations373Image2SMILES: Transformer‐Based Molecular Optical Recognition Engine**53Model agnostic generation of counterfactual explanations for molecules140Molecular design of redox carriers for electrochemical CO 2 capture and concentration81Recent progress in the data-driven discovery of novel photovoltaic materials44Sigma profiles in deep learning: towards a universal molecular descriptor67Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples40Translation between Molecules and Natural Language165From Platform to Knowledge Graph: Evolution of Laboratory Automation108Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis65A review of molecular representation in the age of machine learning368Molecular contrastive learning of representations via graph neural networks851Genetic Optimization of Homogeneous Catalysts47Generative models for molecular discovery: Recent advances and challenges318Machine intelligence for chemical reaction space124Unified Deep Learning Model for Multitask Reaction Predictions with Explanation129Generative machine learning for de novo drug discovery: A systematic review171Recent advances and applications of deep learning methods in materials science1161Machine Learning Applications for Chemical Reactions46Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast73Language models can learn complex molecular distributions173Hydrogen storage metal-organic framework classification models based on crystal graph convolutional neural networks75Fragment‐based drug discovery—the importance of high‐quality molecule libraries150Bidirectional Graphormer for Reactivity Understanding: Neural Network Trained to Reaction Atom-to-Atom Mapping Task47DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design133Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries79Human- and machine-centred designs of molecules and materials for sustainability and decarbonization137SMICLR: Contrastive Learning on Multiple Molecular Representations for Semisupervised and Unsupervised Representation Learning51When machine learning meets molecular synthesis56Deep learning methods for molecular representation and property prediction279SELFIES and the future of molecular string representations261Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis51Machine learning for a sustainable energy future517How machine learning can accelerate electrocatalysis discovery and optimization125Large-scale chemical language representations capture molecular structure and properties575BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations54Graph Neural Networks for Molecules84A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.076Machine learning approaches to predict drug efficacy and toxicity in oncology90Chemical language models for de novo drug design: Challenges and opportunities126New avenues in artificial-intelligence-assisted drug discovery110Hierarchical Molecular Graph Self-Supervised Learning for property prediction170Do Large Language Models Understand Chemistry? A Conversation with ChatGPT147A Perspective on Explanations of Molecular Prediction Models107Deep generative models for 3D molecular structure47Open Macromolecular Genome: Generative Design of Synthetically Accessible Polymers70Testing the limits of SMILES-based de novo molecular generation with curriculum and deep reinforcement learning46Regression Transformer enables concurrent sequence regression and generation for molecular language modelling141Generative Models as an Emerging Paradigm in the Chemical Sciences313Hybrid quantum-classical machine learning for generative chemistry and drug design56Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization53Application of message passing neural networks for molecular property prediction74SELFormer: molecular representation learning via SELFIES language models81The role of machine learning in perovskite solar cell research42DrugEx: Deep Learning Models and Tools for Exploration of Drug-Like Chemical Space41Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review158A Systematic Survey of Chemical Pre-trained Models57Graph-based Molecular Representation Learning59DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications76A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges49Neural scaling of deep chemical models123Deep Generative Models in De Novo Drug Molecule Generation98From intuition to AI: evolution of small molecule representations in drug discovery70Catalyst Energy Prediction with CatBERTa: Unveiling Feature Exploration Strategies through Large Language Models59Shaping the Water-Harvesting Behavior of Metal–Organic Frameworks Aided by Fine-Tuned GPT Models120Multi-modal molecule structure–text model for text-based retrieval and editing179When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges64Materials science in the era of large language models: a perspective99De novo drug design through artificial intelligence: an introduction43Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities189Leveraging large language models for predictive chemistry367Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges48Machine Learning for Sequence and Structure-Based Protein–Ligand Interaction Prediction62A Comprehensive Survey on Deep Graph Representation Learning341Generative AI for designing and validating easily synthesizable and structurally novel antibiotics184Invalid SMILES are beneficial rather than detrimental to chemical language models65Embracing data science in catalysis research117Unleashing the power of generative AI in drug discovery187Application of Transformers in Cheminformatics65t-SMILES: a fragment-based molecular representation framework for de novo ligand design43Machine learning-aided generative molecular design169Advancing drug discovery with deep attention neural networks53WyCryst: Wyckoff inorganic crystal generator framework42MTMol-GPT: De novo multi-target molecular generation with transformer-based generative adversarial imitation learning78Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review59Self-Driving Laboratories for Chemistry and Materials Science674Transformers for Molecular Property Prediction: Lessons Learned from the Past Five Years61Computational and Machine Learning Methods for CO 2 Capture Using Metal–Organic Frameworks74Transformer-based models for chemical SMILES representation: A comprehensive literature review92Comparing SMILES and SELFIES tokenization for enhanced chemical language modeling43MolE: a foundation model for molecular graphs using disentangled attention56A systematic review of deep learning chemical language models in recent era41Data Generation for Machine Learning Interatomic Potentials and Beyond176A review of large language models and autonomous agents in chemistry269CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening106Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance110Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors75Scientific Large Language Models: A Survey on Biological & Chemical Domains122Machine Learning in Polymer Research167Uncertainty quantification with graph neural networks for efficient molecular design47A Deep Generative Model for the Inverse Design of Transition Metal Ligands and Complexes41AI Approaches to Homogeneous Catalysis with Transition Metal Complexes47Drug-target interaction/affinity prediction: Deep learning models and advances review41Graph Neural Networks in Modern AI-Aided Drug Discovery87

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