Molpher: a software framework for systematic chemical space exploration.
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TLDR
Molpher is an open-source software framework for the design of virtual chemical libraries focused on a particular mechanistic class of compounds that produces a path of structurally-related compounds through a process the authors term ‘molecular morphing’.Abstract:
Chemical space is virtual space occupied by all chemically meaningful organic compounds. It is an important concept in contemporary chemoinformatics research, and its systematic exploration is vital to the discovery of either novel drugs or new tools for chemical biology. In this paper, we describe Molpher, an open-source framework for the systematic exploration of chemical space. Through a process we term ‘molecular morphing’, Molpher produces a path of structurally-related compounds. This path is generated by the iterative application of so-called ‘morphing operators’ that represent simple structural changes, such as the addition or removal of an atom or a bond. Molpher incorporates an optimized parallel exploration algorithm, compound logging and a two-dimensional visualization of the exploration process. Its feature set can be easily extended by implementing additional morphing operators, chemical fingerprints, similarity measures and visualization methods. Molpher not only offers an intuitive graphical user interface, but also can be run in batch mode. This enables users to easily incorporate molecular morphing into their existing drug discovery pipelines. Molpher is an open-source software framework for the design of virtual chemical libraries focused on a particular mechanistic class of compounds. These libraries, represented by a morphing path and its surroundings, provide valuable starting data for future in silico and in vitro experiments. Molpher is highly extensible and can be easily incorporated into any existing computational drug design pipeline.read more
Citations
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Journal ArticleDOI
What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery
Edward O. Pyzer-Knapp,Changwon Suh,Rafael Gómez-Bombarelli,Jorge Aguilera-Iparraguirre,Alán Aspuru-Guzik +4 more
TL;DR: A philosophy for defining what constitutes a virtual high-throughput screen is discussed, and the choices that influence decisions at each stage of the computational funnel are investigated, including an in-depth discussion of the generation of molecular libraries.
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Open source molecular modeling
TL;DR: This review categorize, enumerate, and describe available open source software packages for molecular modeling and computational chemistry.
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Defining and Exploring Chemical Spaces
TL;DR: This review provides an overview of some algorithmic approaches to defining and exploring chemical spaces that have the potential to operationalize the process of molecular discovery and emphasizes the potential roles of machine learning and the consideration of synthetic feasibility, which is a prerequisite to ‘closing the loop’.
Journal ArticleDOI
Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES
TL;DR: Inverse design allows the generation of molecules with desirable physical quantities using property optimization as discussed by the authors, but the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming.
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SYBA: Bayesian estimation of synthetic accessibility of organic compounds.
Milan Voršilák,Milan Voršilák,Michal Kolář,Michal Kolář,Ivan Čmelo,Daniel Svozil,Daniel Svozil +6 more
TL;DR: SYBA improves over random forest classification, albeit marginally, and outperforms SAScore and SCScore, but upon the optimization of SAScore threshold (that changes from 6.0 to – 4.5), SAScore yields similar results as SYBA.
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