Molecules in silico: A Graph Description of Chemical Reactions.

Kerber, Adalbert; Laue, Reinhard; Meringer, Markus; Ruecker, C.

doi:10.1002/chin.200733209

Cited by 5 publications

(4 citation statements)

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“…This experiment simulates an in silico combinatorial chemistry library enumeration [ 18 , 33 – 36 ], where molecules are generated, fingerprinted and scored on the fly. The virtual screen is stopped after some amount of time, not because the immense library enumeration has finished.…”

Section: Methodsmentioning

confidence: 99%

Consensus queries in ligand-based virtual screening experiments

Berenger

Meiler

2017

J Cheminform

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BackgroundIn ligand-based virtual screening experiments, a known active ligand is used in similarity searches to find putative active compounds for the same protein target. When there are several known active molecules, screening using all of them is more powerful than screening using a single ligand. A consensus query can be created by either screening serially with different ligands before merging the obtained similarity scores, or by combining the molecular descriptors (i.e. chemical fingerprints) of those ligands.ResultsWe report on the discriminative power and speed of several consensus methods, on two datasets only made of experimentally verified molecules. The two datasets contain a total of 19 protein targets, 3776 known active and ~ 2 × 106 inactive molecules. Three chemical fingerprints are investigated: MACCS 166 bits, ECFP4 2048 bits and an unfolded version of MOLPRINT2D. Four different consensus policies and five consensus sizes were benchmarked.ConclusionsThe best consensus method is to rank candidate molecules using the maximum score obtained by each candidate molecule versus all known actives. When the number of actives used is small, the same screening performance can be approached by a consensus fingerprint. However, if the computational exploration of the chemical space is limited by speed (i.e. throughput), a consensus fingerprint allows to outperform this consensus of scores.

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Section: Methodsmentioning

confidence: 99%

Consensus queries in ligand-based virtual screening experiments

Berenger

Meiler

2017

J Cheminform

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“…This approach yields elementary reaction steps that describe the implied transition state. Other formalisms to describe or enumerate possible chemical reactions exist, such as Dugundji–Ugi, Temkin et al, and Kerber et al, but these all encapsulate overall transformations or general graph rearrangements, and none are analogous to such an ubiquitous chemical idea as “arrow pushing.”…”

Section: Molecular Orbital Reaction Modelmentioning

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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“…As a general data structure, graphs can be used to model many complex relationships between data, and have been widely used in bioinformatics [1], chemoinformatics [2], computer vision [3], video indexing [4], text retrieval [5], efficient database indexing and other fields. The problem of frequent subgraph mining is to find all frequently occurring subgraph structures in a given graph data set.…”

Section: Introductionmentioning

confidence: 99%

SDM: A frequent subgraph mining algorithm for sequential directed graphs

Gao

Wu²

2022

International Conference on Mechanisms and Robotics (ICMAR 2022)

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Current research on frequent subgraph mining mainly focuses on the mining of undirected graphs, but in fact, the research on the mining of directed graphs has more practical significance. A new algorithm for mining frequent subgraphs based on directed graphs, SDM, is proposed. This algorithm uses breadth-first search strategy to mine frequent subgraphs by establishing a hierarchical tree space. And provide an efficient method in the process of frequent subgraph generation and counting. Experimental research shows that compared with other frequent subgraph mining algorithms, SDM has achieved a significant performance improvement.

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Molecules in silico: A Graph Description of Chemical Reactions.

Abstract: ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract, please click on HTML or PDF.

Cited by 5 publications

References 1 publication

Consensus queries in ligand-based virtual screening experiments

Consensus queries in ligand-based virtual screening experiments

Learning to Predict Chemical Reactions

SDM: A frequent subgraph mining algorithm for sequential directed graphs

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