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The Evolution of Data-Driven Modeling in Organic Chemistry.

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TLDR
Data-driven modeling in organic chemistry as discussed by the authors provides a synopsis of the history of data-driven modelling and the terms used to describe these endeavors, as well as a timeline of the steps that led to its current state.
Abstract
Organic chemistry is replete with complex relationships: for example, how a reactant's structure relates to the resulting product formed; how reaction conditions relate to yield; how a catalyst's structure relates to enantioselectivity. Questions like these are at the foundation of understanding reactivity and developing novel and improved reactions. An approach to probing these questions that is both longstanding and contemporary is data-driven modeling. Here, we provide a synopsis of the history of data-driven modeling in organic chemistry and the terms used to describe these endeavors. We include a timeline of the steps that led to its current state. The case studies included highlight how, as a community, we have advanced physical organic chemistry tools with the aid of computers and data to augment the intuition of expert chemists and to facilitate the prediction of structure-activity and structure-property relationships.

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A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

TL;DR: Kraken as mentioned in this paper is a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles.
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Structure-Reactivity-Property Relationships in Covalent Adaptable Networks.

TL;DR: In this article , the authors analyze structure-reactivity-property relationships for several classes of CANs, illustrating both general design principles and the predictive potential of linear free energy relationships (LFERs) applied to CANs.
Journal ArticleDOI

Understanding chemistry: from “heuristic (soft) explanations and reasoning by analogy” to “quantum chemistry”

TL;DR: Hard theories derived from quantum chemistry can be qualitative and quantitative, and the "Houk quadrant" as discussed by the authors is proposed as a helpful categorization tool to distinguish between soft and hard theories.
Journal ArticleDOI

An Ensemble Structure and Physiochemical (SPOC) Descriptor for Machine-Learning Prediction of Chemical Reaction and Molecular Properties.

TL;DR: Inspired by the chemist's vision on molecules, an ensemble descriptor, SPOC, curated on the principles of physical organic chemistry that integrates Structure and Physicochemical property (SPOC) of a molecule is presented.
Journal ArticleDOI

Working at the interfaces of data science and synthetic electrochemistry

TL;DR: Data-science driven electrochemistry as discussed by the authors provides an overview of recent advances in data-science-driven electrochemical with an emphasis on the opportunities and challenges facing this growing subdiscipline.
References
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Journal ArticleDOI

Random Forests

TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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SMILES, a chemical language and information system. 1. introduction to methodology and encoding rules

TL;DR: This chapter discusses the construction of Benzenoid and Coronoid Hydrocarbons through the stages of enumeration, classification, and topological properties in a number of computers used for this purpose.
Posted Content

Generative Adversarial Networks

TL;DR: In this article, a generative adversarial network (GAN) is proposed to estimate generative models via an adversarial process, in which two models are simultaneously trained: a generator G and a discriminator D that estimates the probability that a sample came from the training data rather than G.
Journal ArticleDOI

Random forest: a classification and regression tool for compound classification and QSAR modeling.

TL;DR: It is the combination of relatively high prediction accuracy and its collection of desired features that makes Random Forest uniquely suited for modeling in cheminformatics.
Journal ArticleDOI

Nobel Lecture: Electronic structure of matter-wave functions and density functionals

TL;DR: In this article, an alternative approach to the theory of electronic struc- ture, in which the electron density distribution n(r), rather than the many-electron wave function, plays a central role, is presented.
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