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      Integrating computational lead optimization diagnostics with analog design and candidate selection

      1 , * , 1
      Future Science OA
      Future Science Ltd
      analog design, candidate prioritization, computational diagnostics, decision support, Free-Wilson analysis, lead optimization, medicinal chemistry, potency prediction

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          Combining computational lead optimization diagnostics with analog design and computational approaches for assessing optimization efforts are discussed and the compound optimization monitor is introduced.


          Approaches for compound potency prediction are described and a new analog design algorithm is introduced. Calculation protocols are detailed.

          Results & discussion:

          The study rationale is explained. Compound optimization monitor diagnostics are combined with a thoroughly evaluated approach for compound design and candidate prioritization. The diagnostic scoring scheme is further extended.

          Future perspective:

          Opportunities for practical applications of the integrated computational methodology are described and further development perspectives are discussed.

          Lay abstract

          Compound optimization is a central task in medicinal chemistry, which has many potential pitfalls. Computational approaches that help to better understand and guide chemical optimization efforts are highly desirable, but only a few are currently available. We have aimed to develop a computational methodology that combines, for the first time, the evaluation of progress in chemical optimization with the design of new candidate compounds.

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          Most cited references27

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          A tutorial on support vector regression

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            Graph kernels for chemical informatics.

            Increased availability of large repositories of chemical compounds is creating new challenges and opportunities for the application of machine learning methods to problems in computational chemistry and chemical informatics. Because chemical compounds are often represented by the graph of their covalent bonds, machine learning methods in this domain must be capable of processing graphical structures with variable size. Here, we first briefly review the literature on graph kernels and then introduce three new kernels (Tanimoto, MinMax, Hybrid) based on the idea of molecular fingerprints and counting labeled paths of depth up to d using depth-first search from each possible vertex. The kernels are applied to three classification problems to predict mutagenicity, toxicity, and anti-cancer activity on three publicly available data sets. The kernels achieve performances at least comparable, and most often superior, to those previously reported in the literature reaching accuracies of 91.5% on the Mutag dataset, 65-67% on the PTC (Predictive Toxicology Challenge) dataset, and 72% on the NCI (National Cancer Institute) dataset. Properties and tradeoffs of these kernels, as well as other proposed kernels that leverage 1D or 3D representations of molecules, are briefly discussed.
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              A Mathematical Contribution to Structure-Activity Studies


                Author and article information

                Future Sci OA
                Future Sci OA
                Future Science OA
                Future Science Ltd (London, UK )
                24 January 2020
                March 2020
                24 January 2020
                : 6
                : 3
                : FSO451
                [1 ]Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology & Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53113 Bonn, Germany
                Author notes
                [* ]Author for correspondence: Tel.: +49 228 7369 100; Fax: +49 228 7369 101; bajorath@ 123456bit.uni-bonn.de
                © 2020 Jürgen Bajorath

                This work is licensed under the Creative Commons Attribution 4.0 License

                : 15 November 2019
                : 20 December 2019
                : 24 January 2020
                Page count
                Pages: 17

                analog design,candidate prioritization,computational diagnostics,decision support,free-wilson analysis,lead optimization,medicinal chemistry,potency prediction


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