Reaction prediction: the suggestions of the Beppe program

Sello, Guido

doi:10.1021/ci00010a019

Cited by 35 publications

(26 citation statements)

References 3 publications

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“…Ponec [83] discusses the use of reaction similarity to rationalize various mechanistic features of pericyclic reactions. Sello [84] describes a program that predicts the likely products of specific reactions. Gasteiger [85,86] defines similarity of chemical structures by generalized reaction types and by gross structural features.…”

Section: Reactionsmentioning

confidence: 99%

Approaches to Measure Chemical Similarity – a Review

2003

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Although the concept of similarity is a convenient for humans, a formal definition of similarity between chemical compounds is needed to enable automatic decision‐making. The objective of similarity measures in toxicology and drug design is to allow assessment of chemical activities. The ideal similarity measure should be relevant to the activity of interest. The relevance could be established by exploiting the knowledge about fundamental chemical and biological processes responsible for the activity. Unfortunately, this knowledge is rarely available and therefore different approximations have been developed based on similarity between structures or descriptor values. Various methods are reviewed, ranging from two‐dimensional, three‐dimensional and field approaches to recent methods based on “Atoms in Molecules” theory. All these methods attempt to describe chemical compounds by a set of numerical values and define some means for comparison between them. The review provides analysis of potential pitfalls of this methodology – loss of information in the representations of molecular structures – the relevance of a particular representation and chosen similarity measure to the activity. A brief review of known methods for descriptor selection is also provided. The popular “neighborhood behavior” principle is criticized, since proximity with respect to descriptors does not necessarily mean proximity with respect to activity. Structural similarity should also be used with care, as it does not always imply similar activity, as shown by examples. We remind that similarity measures and classification techniques based on distances rely on certain data distribution assumptions. If these assumptions are not satisfied for a given dataset, the results could be misleading. A discussion on similarity in descriptor space in the context of applicability domain assessment of QSAR models is also provided. Finally, it is shown that descriptor based similarity analysis is prone to errors if the relationship between the activity and the descriptors has not been previously established. A justification for the usage of a particular similarity measure should be provided for every specific activity by expert knowledge or derived by data modeling techniques.

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Section: Reactionsmentioning

confidence: 99%

Approaches to Measure Chemical Similarity – a Review

2003

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“…By the inclusion of latent molecular properties to the family of properties which can be studied in terms of molecular fragments, the conventional tools of molecular similarity analysis, for example, those based on the tools of the quantum similarity methodology of Carbo and several related approaches [14][15][16][17][18][19][20][21][22][23][24], can be effectively employed in a much wider context.…”

Section: Exhibited and Latent Molecular Properties Revisitedmentioning

confidence: 99%

Unexpected expectation values for latent molecular properties

Mezey

2011

J Math Chem

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Latent molecular properties are not exhibited by the given molecular structure but are reproducibly exhibited by the same molecule in a different electronic state or if some molecular interactions have taken place. A consequence of the Holographic Electron Density Theorem, as applied to latent properties, provides a framework that allows an extension of the expectation value formalism, leading to "unexpected" expectation value expressions for latent properties. Connections of special cases of this approach to earlier density matrix extrapolation methods are pointed out.

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“…Other approaches since CAMEO and EROS have contributed their own ideas to the problem. Beppe 10 and Sophia 9 focus on first identifying reactive sites before identifying reactions, though both work with multi-step reactions. ToyChem 11 and Robia 12 build on the EROS idea of physicochemical constraints by explicitly defining reaction energy functions.…”

Section: Introductionmentioning

confidence: 99%

Learning to Predict Chemical Reactions

Kayala

Azencott

Chen

et al. 2011

J. Chem. Inf. Model.

182

178

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Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles respectively are not high-throughput, are not generalizable or scalable, or lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry dataset consisting of 1630 full multi-step reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval, problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of non-productive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system is generalizable, making reasonable predictions over reactants and conditions which the rule-based expert does not handle. A web interface to the machine learning based mechanistic reaction predictor is accessible through our chemoinformatics portal (http://cdb.ics.uci.edu) under the Toolkits section.

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Reaction prediction: the suggestions of the Beppe program

Cited by 35 publications

References 3 publications

Approaches to Measure Chemical Similarity – a Review

Approaches to Measure Chemical Similarity – a Review

Unexpected expectation values for latent molecular properties

Learning to Predict Chemical Reactions

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