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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.

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Citations
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Journal ArticleDOI

What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery

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

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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.
Journal ArticleDOI

SYBA: Bayesian estimation of synthetic accessibility of organic compounds.

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.
References
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Book

Principal Component Analysis

TL;DR: In this article, the authors present a graphical representation of data using Principal Component Analysis (PCA) for time series and other non-independent data, as well as a generalization and adaptation of principal component analysis.
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Principal Component Analysis

TL;DR: Principal component analysis (PCA) as discussed by the authors replaces the p original variables by a smaller number, q, of derived variables, the principal components, which are linear combinations of the original variables.
Journal ArticleDOI

Open Babel: An open chemical toolbox

TL;DR: The implementation of Open Babel is detailed, key advances in the 2.3 release are described, and a variety of uses are outlined both in terms of software products and scientific research, including applications far beyond simple format interconversion.
Journal ArticleDOI

Extended-Connectivity Fingerprints

TL;DR: A description of their implementation has not previously been presented in the literature, and ECFPs can be very rapidly calculated and can represent an essentially infinite number of different molecular features.
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Handbook of Molecular Descriptors

TL;DR: This Users guide notations acronyms list of molecular descriptors contains abbreviations for molecular descriptor values that are useful for counting and topological descriptors calculation.
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