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Donald P. Visco - One of the best experts on this subject based on the ideXlab platform.
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an application of computer aided Molecular design camd using the signature Molecular Descriptor part 1 identification of surface tension reducing agents and the search for shrinkage reducing admixtures
Journal of the American Ceramic Society, 2014Co-Authors: Hamed M. Kayello, Joseph J. Biernacki, Natalia Shlonimskaya, Naresh Kumar Reddy Tadisina, Donald P. ViscoAbstract:The development of new admixtures for concrete is normally an experimental endeavor in that the Molecular scaffolds of existing admixtures are modified and tested. This approach is time consuming, incremental and typically expensive. Alternatively, a computer-aided Molecular design (CAMD) approach is proposed that uses the Signature Molecular Descriptor. CAMD is the application of computer-implemented algorithms that are utilized to design molecules with optimally predicted properties such that they can be tested and evaluated for efficacy. The property of interest here is the surface tension of compounds in aqueous solutions as this property is related to shrinkage in concrete. In particular, we have chosen two classes of compounds, amines and glycol ethers, as they present opportunities for use as shrinkage reducing admixtures (SRAs). By evaluating the initial surface tension reduction in these compounds in solution with water, a number of structure–property conjectures associated with the effect of these compounds were developed. From these conjectures, 14 compounds were identified and utilized as a training set for the CAMD of new compounds. After creating and refining a quantitative structure–property relationship (QSPR) model for surface tension reduction, a structure enumeration algorithm was employed to generate structures outside of the original training set that have optimally predicted properties. In work, the CAMD approach is introduced as well as the identification of new compounds with the greatest predicted impact on the surface tension reduction in water. Furthermore, the surface tension reduction for the newly identified compounds was experimentally evaluated.
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An Application of Computer‐Aided Molecular Design (CAMD) Using the Signature Molecular Descriptor—Part 1. Identification of Surface Tension Reducing Agents and the Search for Shrinkage Reducing Admixtures
Journal of the American Ceramic Society, 2013Co-Authors: Hamed M. Kayello, Joseph J. Biernacki, Natalia Shlonimskaya, Naresh Kumar Reddy Tadisina, Donald P. ViscoAbstract:The development of new admixtures for concrete is normally an experimental endeavor in that the Molecular scaffolds of existing admixtures are modified and tested. This approach is time consuming, incremental and typically expensive. Alternatively, a computer-aided Molecular design (CAMD) approach is proposed that uses the Signature Molecular Descriptor. CAMD is the application of computer-implemented algorithms that are utilized to design molecules with optimally predicted properties such that they can be tested and evaluated for efficacy. The property of interest here is the surface tension of compounds in aqueous solutions as this property is related to shrinkage in concrete. In particular, we have chosen two classes of compounds, amines and glycol ethers, as they present opportunities for use as shrinkage reducing admixtures (SRAs). By evaluating the initial surface tension reduction in these compounds in solution with water, a number of structure–property conjectures associated with the effect of these compounds were developed. From these conjectures, 14 compounds were identified and utilized as a training set for the CAMD of new compounds. After creating and refining a quantitative structure–property relationship (QSPR) model for surface tension reduction, a structure enumeration algorithm was employed to generate structures outside of the original training set that have optimally predicted properties. In work, the CAMD approach is introduced as well as the identification of new compounds with the greatest predicted impact on the surface tension reduction in water. Furthermore, the surface tension reduction for the newly identified compounds was experimentally evaluated.
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Computer-aided Molecular design using the Signature Molecular Descriptor: Application to solvent selection
Computers & Chemical Engineering, 2010Co-Authors: Derick C. Weis, Donald P. ViscoAbstract:Abstract There is a growing demand to develop more environmentally friendly solvents to reduce costs and comply with regulation. Researchers at GlaxoSmithKline (GSK) have developed a solvent selection guide that ranks 47 frequently used solvents from 1 to 10 in five areas related to environmental compatibility. In this work, we apply a computer-aided Molecular design method known as inverse design with the Signature Molecular Descriptor to identify additional potentially green solvents outside of GSK's list. Applying this approach is much quicker, less expensive and allows for a more comprehensive search for the most suitable candidates than working with experimental data alone. We present results for solvents with optimal predicted properties that span the classes from the 47 compounds in the GSK solvent selection guide and include several which are hybrids that cross-cut amongst classes. Additionally, our technique “rediscovers” the known green solvent ethyl lactate through this method by combining different solvent classes.
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potential glucocorticoid receptor ligands with pulmonary selectivity using i qsar with the signature Molecular Descriptor
Chemical Biology & Drug Design, 2008Co-Authors: Joshua D Jackson, Derick C. Weis, Donald P. ViscoAbstract:We intend in this research to establish a rational method for the development of novel glucocorticoid receptor ligands to more effectively prevent respiratory inflammation. Corticosteroids, a class of steroid hormones, are naturally inclined to bind to the glucocorticoid receptor and, in this research, are the basis for exploring other novel and non-intuitive structures. To be more effective than currently available medications, novel compounds must be highly selective toward the lungs and must be inactivated when exposed to the main circulation, thus preventing the participation of the ligand in other systems and consequently reducing systemic side-effects. We look to use the inverse-quantitative structure-activity relationship algorithm with the Signature Molecular Descriptor to generate new ligands based upon the structures and activities of 65 experimentally studied corticosteroids. Inverse-quantitative structure-activity relationship explore many possible combinations of atom connectivity while structural filters and other scoring approaches are used to predict and identify the most promising candidates for further study. Properties explored include high receptor binding affinity, high systemic clearance, high plasma protein binding and low oral bioavailability. Among more than 300 million potential candidates generated, 84 high priority compounds with properties predicted to be at least as or more effective than currently available corticosteroids have been identified with this procedure.
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Data mining PubChem using a support vector machine with the Signature Molecular Descriptor: classification of factor XIa inhibitors.
Journal of molecular graphics & modelling, 2008Co-Authors: Derick C. Weis, Donald P. Visco, Jean-loup FaulonAbstract:The amount of high-throughput screening (HTS) data readily available has significantly increased because of the PubChem project (http://pubchem.ncbi.nlm.nih.gov/). There is considerable opportunity for data mining of small molecules for a variety of biological systems using cheminformatic tools and the resources available through PubChem. In this work, we trained a support vector machine (SVM) classifier using the Signature Molecular Descriptor on factor XIa inhibitor HTS data. The optimal number of Signatures was selected by implementing a feature selection algorithm of highly correlated clusters. Our method included an improvement that allowed clusters to work together for accuracy improvement, where previous methods have scored clusters on an individual basis. The resulting model had a 10-fold cross-validation accuracy of 89%, and additional validation was provided by two independent test sets. We applied the SVM to rapidly predict activity for approximately 12 million compounds also deposited in PubChem. Confidence in these predictions was assessed by considering the number of Signatures within the training set range for a given compound, defined as the overlap metric. To further evaluate compounds identified as active by the SVM, docking studies were performed using AutoDock. A focused database of compounds predicted to be active was obtained with several of the compounds appreciably dissimilar to those used in training the SVM. This focused database is suitable for further study. The data mining technique presented here is not specific to factor XIa inhibitors, and could be applied to other bioassays in PubChem where one is looking to expand the search for small molecules as chemical probes.
Jean-loup Faulon - One of the best experts on this subject based on the ideXlab platform.
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Stereo signature Molecular Descriptor
Journal of Chemical Information and Modeling, 2013Co-Authors: Pablo Carbonell, Lars Carlsson, Jean-loup FaulonAbstract:We present an algorithm to compute Molecular graph Descriptors considering the stereochemistry of the Molecular structure based on our previously introduced signature Molecular Descriptor. The algorithm can generate two types of Descriptors, one which is compliant with the Cahn-Ingold-Prelog priority rules, including complex stereochemistry structures such as fullerenes, and a computationally efficient one based on our previous definition of a directed acyclic graph that is augmented to a chiral Molecular graph. The performance of the algorithm in terms of speed as a canonicalizer as well as in modeling and predicting bioactivity is evaluated, showing an overall better performance than other Molecular Descriptors, which is particularly relevant in modeling stereoselective biochemical reactions. The complete source code of the stereo signature Molecular Descriptor is available for download under an open-source license at http://molsig.sourceforge.net.
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Data mining PubChem using a support vector machine with the Signature Molecular Descriptor: classification of factor XIa inhibitors.
Journal of molecular graphics & modelling, 2008Co-Authors: Derick C. Weis, Donald P. Visco, Jean-loup FaulonAbstract:The amount of high-throughput screening (HTS) data readily available has significantly increased because of the PubChem project (http://pubchem.ncbi.nlm.nih.gov/). There is considerable opportunity for data mining of small molecules for a variety of biological systems using cheminformatic tools and the resources available through PubChem. In this work, we trained a support vector machine (SVM) classifier using the Signature Molecular Descriptor on factor XIa inhibitor HTS data. The optimal number of Signatures was selected by implementing a feature selection algorithm of highly correlated clusters. Our method included an improvement that allowed clusters to work together for accuracy improvement, where previous methods have scored clusters on an individual basis. The resulting model had a 10-fold cross-validation accuracy of 89%, and additional validation was provided by two independent test sets. We applied the SVM to rapidly predict activity for approximately 12 million compounds also deposited in PubChem. Confidence in these predictions was assessed by considering the number of Signatures within the training set range for a given compound, defined as the overlap metric. To further evaluate compounds identified as active by the SVM, docking studies were performed using AutoDock. A focused database of compounds predicted to be active was obtained with several of the compounds appreciably dissimilar to those used in training the SVM. This focused database is suitable for further study. The data mining technique presented here is not specific to factor XIa inhibitors, and could be applied to other bioassays in PubChem where one is looking to expand the search for small molecules as chemical probes.
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genome scale enzyme metabolite and drug target interaction predictions using the signature Molecular Descriptor
Bioinformatics, 2008Co-Authors: Jean-loup Faulon, Shawn Martin, Milind Misra, Ken Sale, Rajat SapraAbstract:Motivation: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. There is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein–chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. Results: Our method relies on expressing proteins and chemicals with a common cheminformatics representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets. Availability and Contact: For questions, paper reprints, please contact Jean-Loup Faulon at jfaulon@sandia.gov. Additional information on the signature Molecular Descriptor and codes can be downloaded at: http://www.cs.sandia.gov/~jfaulon/publication-signature.html Supplementary information: Supplementary data are available at Bioinformatics online.
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designing novel polymers with targeted properties using the signature Molecular Descriptor
Journal of Chemical Information and Modeling, 2006Co-Authors: Michael W Brown, Shawn Martin, Mark Daniel Rintoul, Jean-loup FaulonAbstract:A method for solving the inverse quantitative structure-property relationship (QSPR) problem is presented which facilitates the design of novel polymers with targeted properties. Here, we demonstrate the efficacy of the approach using the targeted design of polymers exhibiting a desired glass transition temperature, heat capacity, and density. We present novel QSPRs based on the signature Molecular Descriptor capable of predicting glass transition temperature, heat capacity, density, molar volume, and cohesive energies of linear homopolymers with cross-validation squared correlation coefficients ranging between 0.81 and 0.95. Using these QSPRs, we show how the inverse problem can be solved to design poly(N-methyl hexamethylene sebacamide) despite the fact that the polymer was used not used in the training of this model.
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the signature Molecular Descriptor 5 the design of hydrofluoroether foam blowing agents using inverse qsar
Industrial & Engineering Chemistry Research, 2005Co-Authors: Derick C. Weis, Jean-loup Faulon, Richard C Leborne, Donald P. ViscoAbstract:In this work, a novel technique for Molecular design is explored by generating compounds to replace R-141b in polyurethane foam blowing applications. This technique, which is known as the inverse quantitative structure−activity relationship (I-QSAR) method, is based on solving the inverse problem of Molecular design, using a newly developed Descriptor called Signature. In this work, we optimize the properties of the candidate solutions based on the normal boiling point and the vapor-phase thermal conductivity. After generating more than 3 million solutions with this technique, we have identified seven compounds for further study. Unlike other inverse design techniques, I-QSAR with Signature does not use a template compound and, thus, nonintuitive candidates with optimal predicted properties can result. The seven best candidates that form the focused database include straight chains and rings of a variety of sizes with one or two O atoms in the ring.
Ramon Carbodorca - One of the best experts on this subject based on the ideXlab platform.
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notes on quantitative structure properties relationships qspr part 2 the role of the number of atoms as a Molecular Descriptor
Journal of Computational Chemistry, 2009Co-Authors: Ramon Carbodorca, Ana Gallegos SalinerAbstract:A previous analysis performed in our laboratory about the polynomial dependency of the atomic quantum self-similarity measures on the atomic number, together with recent publications on quantitative structure-properties relationships (QSPR), based on the number of Molecular atoms, published by various authors, have driven us to show here that a simplified form of the fundamental quantum QSPR (QQSPR) equation, permits to theoretically demonstrate the important, but obvious, role of the number of atoms in a molecule, as a possible Molecular Descriptor. A discussion of the practical use of the number of atoms in QSPR is also given at the end, which also contains a discussion on the role of Ockham's razor in Descriptor simplification choices.
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Molecular basis of quantitative structure properties relationships qspr a quantum similarity approach
Journal of Computer-aided Molecular Design, 1999Co-Authors: Robert Ponec, Lluís Amat, Ramon CarbodorcaAbstract:Since the dawn of quantitative structure-properties relationships (QSPR), empirical parameters related to structural, electronic and hydrophobic Molecular properties have been used as Molecular Descriptors to determine such relationships. Among all these parameters, Hammett σ constants and the logarithm of the octanol- water partition coefficient, log P, have been massively employed in QSPR studies. In the present paper, a new Molecular Descriptor, based on quantum similarity measures (QSM), is proposed as a general substitute of these empirical parameters. This work continues previous analyses related to the use of QSM to QSPR, introducing Molecular quantum self-similarity measures (MQS-SM) as a single working parameter in some cases. The use of MQS-SM as a Molecular Descriptor is first confirmed from the correlation with the aforementioned empirical parameters. The Hammett equation has been examined using MQS-SM for a series of substituted carboxylic acids. Then, for a series of aliphatic alcohols and acetic acid esters, log P values have been correlated with the self-similarity measure between density functions in water and octanol of a given molecule. And finally, some examples and applications of MQS-SM to determine QSAR are presented. In all studied cases MQS-SM appeared to be excellent Molecular Descriptors usable in general QSPR applications of chemical interest.
Milan Randić - One of the best experts on this subject based on the ideXlab platform.
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On of Molecular similarity based on a single Molecular Descriptor
Chemical Physics Letters, 2014Co-Authors: Milan RandićAbstract:Abstract We consider the characterization of Molecular similarity through a single Molecular Descriptor, instead of the customary use of sets of structural invariants to characterize individual molecules. Moreover, we require that the ‘similarity’ Descriptor be conceptually and computationally simple so that it is suitable for screening huge combinatorial libraries in search for target compounds. We have outlined one such general approach for construction of ‘similarity’ Descriptors, which is illustrated on the set of 35 nonane constitutional isomers.
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Wiener-Hosoya index--a novel graph theoretical Molecular Descriptor.
Journal of chemical information and computer sciences, 2004Co-Authors: Milan RandićAbstract:We describe the construction of a novel Molecular Descriptor, called the Wiener-Hosoya index, in view of its structural relationship to both the Wiener number W and the Hosoya topological index Z. It is shown that this index has a smaller degeneracy than many simple topological indices, including W, Z, and the connectivity index chi. In a way the index can be viewed as a particular generalization of the Wiener number.
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Novel Molecular Descriptor for structure—property studies
Chemical Physics Letters, 1993Co-Authors: Milan RandićAbstract:Abstract We report on a novel Molecular Descriptor for the study of structure—property relationships which appears to complement existing Molecular Descriptors. The Descriptor is derived from a matrix associated with Molecular graph, the entries of which are related to the Wiener number. Use of the novel Descriptor is illustrated in a correlation with the Molecular surface areas in heptane isomers.
Hamed M. Kayello - One of the best experts on this subject based on the ideXlab platform.
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an application of computer aided Molecular design camd using the signature Molecular Descriptor part 1 identification of surface tension reducing agents and the search for shrinkage reducing admixtures
Journal of the American Ceramic Society, 2014Co-Authors: Hamed M. Kayello, Joseph J. Biernacki, Natalia Shlonimskaya, Naresh Kumar Reddy Tadisina, Donald P. ViscoAbstract:The development of new admixtures for concrete is normally an experimental endeavor in that the Molecular scaffolds of existing admixtures are modified and tested. This approach is time consuming, incremental and typically expensive. Alternatively, a computer-aided Molecular design (CAMD) approach is proposed that uses the Signature Molecular Descriptor. CAMD is the application of computer-implemented algorithms that are utilized to design molecules with optimally predicted properties such that they can be tested and evaluated for efficacy. The property of interest here is the surface tension of compounds in aqueous solutions as this property is related to shrinkage in concrete. In particular, we have chosen two classes of compounds, amines and glycol ethers, as they present opportunities for use as shrinkage reducing admixtures (SRAs). By evaluating the initial surface tension reduction in these compounds in solution with water, a number of structure–property conjectures associated with the effect of these compounds were developed. From these conjectures, 14 compounds were identified and utilized as a training set for the CAMD of new compounds. After creating and refining a quantitative structure–property relationship (QSPR) model for surface tension reduction, a structure enumeration algorithm was employed to generate structures outside of the original training set that have optimally predicted properties. In work, the CAMD approach is introduced as well as the identification of new compounds with the greatest predicted impact on the surface tension reduction in water. Furthermore, the surface tension reduction for the newly identified compounds was experimentally evaluated.
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An Application of Computer‐Aided Molecular Design (CAMD) Using the Signature Molecular Descriptor—Part 1. Identification of Surface Tension Reducing Agents and the Search for Shrinkage Reducing Admixtures
Journal of the American Ceramic Society, 2013Co-Authors: Hamed M. Kayello, Joseph J. Biernacki, Natalia Shlonimskaya, Naresh Kumar Reddy Tadisina, Donald P. ViscoAbstract:The development of new admixtures for concrete is normally an experimental endeavor in that the Molecular scaffolds of existing admixtures are modified and tested. This approach is time consuming, incremental and typically expensive. Alternatively, a computer-aided Molecular design (CAMD) approach is proposed that uses the Signature Molecular Descriptor. CAMD is the application of computer-implemented algorithms that are utilized to design molecules with optimally predicted properties such that they can be tested and evaluated for efficacy. The property of interest here is the surface tension of compounds in aqueous solutions as this property is related to shrinkage in concrete. In particular, we have chosen two classes of compounds, amines and glycol ethers, as they present opportunities for use as shrinkage reducing admixtures (SRAs). By evaluating the initial surface tension reduction in these compounds in solution with water, a number of structure–property conjectures associated with the effect of these compounds were developed. From these conjectures, 14 compounds were identified and utilized as a training set for the CAMD of new compounds. After creating and refining a quantitative structure–property relationship (QSPR) model for surface tension reduction, a structure enumeration algorithm was employed to generate structures outside of the original training set that have optimally predicted properties. In work, the CAMD approach is introduced as well as the identification of new compounds with the greatest predicted impact on the surface tension reduction in water. Furthermore, the surface tension reduction for the newly identified compounds was experimentally evaluated.