Defining and Exploring Chemical Spaces

Coley, Connor W.

doi:10.1016/j.trechm.2020.11.004

Cited by 89 publications

(86 citation statements)

References 106 publications

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“…Molecular (as with any other kind of) similarity [70][71][72] is a somewhat elusive but, importantly, unsupervised concept in which we seek a metric to describe, in some sense, how closely related two entities are to each other from their structure or appearance alone. The set of all small molecules of possible interest for some purpose, subject to constraints such as commercial availability [73], synthetic accessibility [74,75], or "druglikeness" [76,77], is commonly referred to "chemical space", and it is very large [78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97]. In cheminformatics the concept of similarity is widely used to prioritize the choice of molecules "similar" to an initial molecule (usually a "hit" with a given property or activity in an assay of interest) from this chemical space or by comparison with those in a database, on the grounds that "similar" molecular structures tend to have "similar" bioactivities [98].…”

Section: Molecular Similaritymentioning

confidence: 99%

FragNet, a Contrastive Learning-Based Transformer Model for Clustering, Interpreting, Visualizing, and Navigating Chemical Space

Shrivastava

Kell

2021

Molecules

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The question of molecular similarity is core in cheminformatics and is usually assessed via a pairwise comparison based on vectors of properties or molecular fingerprints. We recently exploited variational autoencoders to embed 6M molecules in a chemical space, such that their (Euclidean) distance within the latent space so formed could be assessed within the framework of the entire molecular set. However, the standard objective function used did not seek to manipulate the latent space so as to cluster the molecules based on any perceived similarity. Using a set of some 160,000 molecules of biological relevance, we here bring together three modern elements of deep learning to create a novel and disentangled latent space, viz transformers, contrastive learning, and an embedded autoencoder. The effective dimensionality of the latent space was varied such that clear separation of individual types of molecules could be observed within individual dimensions of the latent space. The capacity of the network was such that many dimensions were not populated at all. As before, we assessed the utility of the representation by comparing clozapine with its near neighbors, and we also did the same for various antibiotics related to flucloxacillin. Transformers, especially when as here coupled with contrastive learning, effectively provide one-shot learning and lead to a successful and disentangled representation of molecular latent spaces that at once uses the entire training set in their construction while allowing “similar” molecules to cluster together in an effective and interpretable way.

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Section: Molecular Similaritymentioning

confidence: 99%

FragNet, a Contrastive Learning-Based Transformer Model for Clustering, Interpreting, Visualizing, and Navigating Chemical Space

Shrivastava

Kell

2021

Molecules

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“…Beyond the significant increase of chemical compounds that can be accessed (either in-stock or readily accessible after synthesis) a common trend now is the generation of chemical compounds designed de novo using machine learning. This has been reviewed recently in excellent review papers [ 8 , 46 ].…”

Section: Open Resources To Expand and Describe The Chemical Spacementioning

confidence: 99%

“…Arguably, it has been commented that "different chemical spaces" are associated by different types of molecules (small molecules, biologics, polymers, materials, etc. [ 46 ]). Under the later notion, molecules with different nature (like polymers, materials, etc.)…”

Section: Open Resources To Expand and Describe The Chemical Spacementioning

confidence: 99%

Progress on open chemoinformatic tools for expanding and exploring the chemical space

Medina-Franco

Sánchez-Cruz

López-López

et al. 2021

J Comput Aided Mol Des

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The concept of chemical space is a cornerstone in chemoinformatics, and it has broad conceptual and practical applicability in many areas of chemistry, including drug design and discovery. One of the most considerable impacts is in the study of structure–property relationships where the property can be a biological activity or any other characteristic of interest to a particular chemistry discipline. The chemical space is highly dependent on the molecular representation that is also a cornerstone concept in computational chemistry. Herein, we discuss the recent progress on chemoinformatic tools developed to expand and characterize the chemical space of compound data sets using different types of molecular representations, generate visual representations of such spaces, and explore structure–property relationships in the context of chemical spaces. We emphasize the development of methods and freely available tools focusing on drug discovery applications. We also comment on the general advantages and shortcomings of using freely available and easy-to-use tools and discuss the value of using such open resources for research, education, and scientific dissemination.

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“…Empirical physical and chemical rules are first employed to acquire the list of potential materials for study, which are then experimentally synthesized and characterized. The measured properties of these materials will be compared with each other, through which new knowledge is generated to refine the empirical rules [9] . This trial-and-error process is labor-intensive and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Generative models for inverse design of inorganic solid materials

Chen¹,

Zhang²,

Nie³

et al. 2021

JMI

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Overwhelming evidence has been accumulating that materials informatics can provide a novel solution for materials discovery. While the conventional approach to innovation relies mainly on experimentation, the generative models stemming from the field of machine learning can realize the long-held dream of inverse design, where properties are mapped to the chemical structures. In this review, we introduce the general aspects of inverse materials design and provide a brief overview of two generative models, variational autoencoder and generative adversarial network, which can be utilized to generate and optimize inorganic solid materials according to their properties. Reversible representation schemes for generative models are compared between molecular and crystalline structures, and challenges in regard to the latter are also discussed. Finally, we summarize the recent application of generative models in the exploration of chemical space with compositional and configurational degrees of freedom, and potential future directions are speculatively outlined.

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Defining and Exploring Chemical Spaces

Cited by 89 publications

References 106 publications

FragNet, a Contrastive Learning-Based Transformer Model for Clustering, Interpreting, Visualizing, and Navigating Chemical Space

FragNet, a Contrastive Learning-Based Transformer Model for Clustering, Interpreting, Visualizing, and Navigating Chemical Space

Progress on open chemoinformatic tools for expanding and exploring the chemical space

Generative models for inverse design of inorganic solid materials

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