SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules

By David Weininger. 1988

Subfields: Computational Chemistry & Drug Discovery

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Citations in the corpus

A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets (2015, methodology): “A reaction transformation language (SMIRKS) (52) was used to encode the transformations, and the StarDrop software platform (53) was used to apply the modification to a parent (starting) fragment using criteria supplied by the researchers.”
Galactica: A Large Language Model for Science (2022, background): “SMILES is a line notation which represents chemical structure as a sequence of characters (Weininger, 1988).”
Convolutional Networks on Graphs for Learning Molecular Fingerprints (2015, methodology): “Experimental setup Our pipeline takes as input the SMILES [30] string encoding of each molecule, which is then converted into a graph using RDKit [20].”
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control (2016, methodology): “Organic molecules can be encoded using the commonly used SMILES representation (Weininger, 1970).”
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (2018, neutral): “We represent molecules directly as molecular graphs, which are more robust than intermediate representations such as simpliﬁed molecular-input line-entry system (SMILES) [40], a text-based representation that is widely used in previous works [9, 22, 4, 15, 38, 27, 34].”
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization (2018, methodology): “Earlier approaches represented molecules as SMILES strings (Weininger, 1988), while more recent methods represented them as graphs.”
Junction Tree Variational Autoencoder for Molecular Graph Generation (2018, methodology): “Speciﬁcally, these models start by generating SMILES (Weininger, 1988), a linear string notation used in chemistry to describe molecular structures.”
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction (2017, background): “SMILES is a ”chemical language” [33] that encodes structural information into a compact text representation.”
Grammar Variational Autoencoder (2017, methodology): “For instance, molecules can be represented by so-called SMILES strings (Weininger, 1988) and Gómez-Bombarelli et al. (2016b) has recently developed a generative model for molecules based on SMILES strings that uses GRUs and DCNNs.”
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules (2016, neutral): “Figure 2a shows a kernel density estimate of each dimension when encoding a set of 5000 randomly selected ZINC molecules from outside the training set.”
Machine learning of molecular electronic properties in chemical compound space (2013, methodology): “Based on the string representation (SMILES [16, 17]) of molecules in the database, we used the universal force field [18] to generate reasonable Cartesian molecular geometries, as implemented in OpenBabel [19].”
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks (2017, background): “54 To indicate rings, a number is introduced at the two atoms where the ring is closed.”