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Nishanth G. Chemmangattuvalappil - One of the best experts on this subject based on the ideXlab platform.
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a systematic Molecular Design framework with the consideration of competing solvent recovery processes
Industrial & Engineering Chemistry Research, 2019Co-Authors: Denny K S Ng, Nishanth G. ChemmangattuvalappilAbstract:This Article presents a Computer Aided Molecular Design (CAMD) framework that considers both the solute extraction and the solvent recovery steps simultaneously during the solvent Design. The choice of a solvent has a significant role not only on the effectiveness of the process, but also on the safety, health, and environmental impact. In addition, the energy required for solvent regeneration is dependent on both the chosen solvent and its recovery techniques. Therefore, we have incorporated physicochemical property targets, safety, health, and environmental (SHE) aspects and utilities cost needed to recover the solvent during the Design stage. Fuzzy Analytic Hierarchy Process (FAHP) weighting approach is applied to assign consistent weights to the multiple objective functions. Through this approach, the Designed solvents with favorable functionalities can be recovered through an economically efficient and environment friendly process. The developed methodology is used to Design a solvent to be used in p...
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Integration of Fuzzy Analytic Hierarchy Process into multi-objective Computer Aided Molecular Design
Computers & Chemical Engineering, 2018Co-Authors: Jecksin Ooi, Michael Angelo B. Promentilla, Raymond R. Tan, Nishanth G. ChemmangattuvalappilAbstract:Abstract In this paper, a novel Computer Aided Molecular Design (CAMD) framework is developed to solve multi-objective Molecular Design problems. CAMD can be formulated as a multi-objective optimisation problem when there are multiple target properties to be optimised simultaneously. A major obstacle faced by multi-objective CAMD problems is the difficulty in assigning weighting factors to the target properties, since the relative importance of these factors is not always defined. It is particularly difficult to compare target properties which belong to different categories, such as physicochemical, safety, health and environmental properties, on a common scale. This paper presents a systematic CAMD algorithm built on Fuzzy Analytic Hierarchy Process (FAHP) to deal with the ambiguity involved in evaluating the weights of target properties in multi-objective CAMD problem. Instead of using exact numerical values, FAHP approach expresses the pairwise comparison of target properties through triangular fuzzy numbers, which allow the degree of confidence of decision maker to be quantified. Hence, the proposed approach can address the uncertainties arising from ambiguity involved during value judgement elicitation in multi-objective CAMD problems. The solutions generated provide a better balance of performance for a set of identified target properties. The proposed methodology is illustrated through a case study on Designing a better solvent for extracting residual oil from palm pressed fibre.
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a systematic methodology for multi objective Molecular Design via analytic hierarchy process
Process Safety and Environmental Protection, 2017Co-Authors: Jecksin Ooi, Michael Angelo B. Promentilla, Raymond R. Tan, Nishanth G. ChemmangattuvalappilAbstract:Abstract This paper presents a novel methodology for solving multi-objective Computer Aided Molecular Design (CAMD) problems. One of the major challenges in multi-objective CAMD problems is the subjectivity involved in assigning the weighting factors to each property that is optimised. It is difficult to define the relative importance of each property in CAMD problems as target properties that belong to different categories cannot be compared on a common scale. It is crucial to solve this issue as distinct solutions will be generated due to different weighting factors of each property. In this work, a systematic framework that combines CAMD and Analytic Hierarchy Process (AHP) is developed to deal with the ambiguities involved in assessing the relative importance weightings of target properties in multi-objective Molecular Design problem. Through AHP, Molecular Design problems can be approached as a hierarchical decision structure and different categories of target properties are combined in the same analysis. Besides, AHP has the ability to generate numerical priorities from the subjective knowledge in evaluating the relative importance of properties using pairwise comparison. A case study on solvent Design for oil extraction from palm pressed fibre is solved to illustrate the proposed methodology.
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a novel methodology for property based Molecular Design using multiple topological indices
Industrial & Engineering Chemistry Research, 2013Co-Authors: Nishanth G. Chemmangattuvalappil, Mario R EdenAbstract:In this work, we are introducing an algorithm that can be used for solving Molecular Design problems of reactive systems on a property-based platform. The property clustering technique is extended to identify a set of target properties for the components in the system that provides the optimum process performance. Once the property targets have been identified, a Molecular Design problem can be formulated to identify the potential candidate molecules that meet the targets identified in the previous problem. The Molecular Design involves the identification of potential molecules possible from the specific types of reactions in the process. To Design molecules, a recently introduced concept known as Molecular signature descriptors has been used. The Molecular signatures can be tailored to track the changes in Molecular groups in a molecule resulting from different types of chemical reactions. The changes in the chemical structure can be correlated with the changes in the properties of the molecule. Therefor...
Woo Youn Kim - One of the best experts on this subject based on the ideXlab platform.
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scaffold based Molecular Design with a graph generative model
Chemical Science, 2020Co-Authors: Jaechang Lim, Sangyeon Hwang, Seungsu Kim, Seokhyun Moon, Woo Youn KimAbstract:Searching for new molecules in areas like drug discovery often starts from the core structures of known molecules. Such a method has called for a strategy of Designing derivative compounds retaining a particular scaffold as a substructure. On this account, our present work proposes a graph generative model that targets its use in scaffold-based Molecular Design. Our model accepts a Molecular scaffold as input and extends it by sequentially adding atoms and bonds. The generated molecules are then guaranteed to contain the scaffold with certainty, and their properties can be controlled by conditioning the generation process on desired properties. The learned rule of extending molecules can well generalize to arbitrary kinds of scaffolds, including those unseen during learning. In the conditional generation of molecules, our model can simultaneously control multiple chemical properties despite the search space constrained by fixing the substructure. As a demonstration, we applied our model to Designing inhibitors of the epidermal growth factor receptor and show that our model can employ a simple semi-supervised extension to broaden its applicability to situations where only a small amount of data is available.
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scaffold based Molecular Design using graph generative model
Chemical Science, 2020Co-Authors: Jaechang Lim, Sangyeon Hwang, Seungsu Kim, Seokhyun Moon, Woo Youn KimAbstract:Searching for new molecules in areas like drug discovery often starts from the core structures of known molecules. Such a method has called for a strategy of Designing derivative compounds retaining a particular scaffold as a substructure. On this account, our present work proposes a graph generative model that targets its use in scaffold-based Molecular Design. Our model accepts a Molecular scaffold as input and extends it by sequentially adding atoms and bonds. The generated molecules are then guaranteed to contain the scaffold with certainty, and their properties can be controlled by conditioning the generation process on desired properties. The learned rule of extending molecules can well generalize to arbitrary kinds of scaffolds, including those unseen during learning. In the conditional generation of molecules, our model can simultaneously control multiple chemical properties despite the search space constrained by fixing the substructure. As a demonstration, we applied our model to Designing inhibitors of the epidermal growth factor receptor and show that our model can employ a simple semi-supervised extension to broaden its applicability to situations where only a small amount of data is available.
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Molecular generative model based on conditional variational autoencoder for de novo Molecular Design
Journal of Cheminformatics, 2018Co-Authors: Jaechang Lim, Seongok Ryu, Jinwoo Kim, Woo Youn KimAbstract:We propose a Molecular generative model based on the conditional variational autoencoder for de novo Molecular Design. It is specialized to control multiple Molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules with five target properties. We were also able to adjust a single property without changing the others and to manipulate it beyond the range of the dataset.
Rafiqul Gani - One of the best experts on this subject based on the ideXlab platform.
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a machine learning based computer aided Molecular Design screening methodology for fragrance molecules
Computers & Chemical Engineering, 2018Co-Authors: Lei Zhang, Haitao Mao, Linlin Liu, Rafiqul GaniAbstract:Abstract Although the business of flavors and fragrances has become a multibillion dollar market, the Design/screening of fragrances still relies on the experience of specialists as well as available odor databases. Potentially better products, however, could be missed when employing this approach. Therefore, a computer-aided Molecular Design/screening method is developed in this work for the Design and screening of fragrance molecules as an important first step. In this method, the odor of the molecules are predicted using a data driven machine learning approach, while a group contribution based method is employed for prediction of important physical properties, such as, vapor pressure, solubility parameter and viscosity. A MILP/MINLP model is established for the Design and screening of fragrance molecules. Decomposition-based solution approach is used to obtain the optimal result. Finally, case studies are presented to highlight the application of the proposed fragrance Design/screening method.
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Generic mathematical programming formulation and solution for computer-aided Molecular Design
Computers & Chemical Engineering, 2015Co-Authors: Lei Zhang, Stefano Cignitti, Rafiqul GaniAbstract:Abstract This short communication presents a generic mathematical programming formulation for computer-aided Molecular Design (CAMD). A given CAMD problem, based on target properties, is formulated as a mixed integer linear/non-linear program (MILP/MINLP). The mathematical programming model presented here, which is formulated as an MILP/MINLP problem, considers first-order and second-order Molecular groups for Molecular structure representation and property estimation. It is shown that various CAMD problems can be formulated and solved through this model.
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a computer aided Molecular Design framework for crystallization solvent Design
Chemical Engineering Science, 2006Co-Authors: Arunprakash T. Karunanithi, Luke E. K. Achenie, Rafiqul GaniAbstract:One of the key decisions in Designing solution crystallization processes is the selection of solvents. In this paper, we present a computer-aided Molecular Design (CAMD) framework for the Design and selection of solvents and/or anti-solvents for solution crystallization. The CAMD problem is formulated as a mixed integer nonlinear programming (MINLP) model. Although, the model allows any combination of performance objectives and property constraints, in the case studies, potential recovery was considered as the performance objective. The latter, needs to be maximized, while other solvent property requirements such as solubility, crystal morphology, flashpoint, toxicity, viscosity, normal boiling and melting point are posed as constraints. All the properties are estimated using group contribution methods. The MINLP model is then solved using a decomposition approach to obtain optimal solvent molecules. Solvent Design and selection for two types of solution crystallization processes namely cooling crystallization and drowning out crystallization are presented. In the first case study, the Design of single compound solvent for crystallization of ibuprofen, which is an important pharmaceutical compound, is addressed. One of the important issues namely, the effect of solvent on the shape of ibuprofen crystals is also considered in the MINLP model. The second case study is a mixture Design problem where an optimal solvent/anti-solvent mixture is Designed for crystallization of ibuprofen by the drowning out technique. For both case studies the performance of the solvents are verified qualitatively through SLE diagrams.
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a multi step and multi level approach for computer aided Molecular Design
Computers & Chemical Engineering, 2000Co-Authors: Peter Mathias Harper, Rafiqul GaniAbstract:Abstract A general multi-step approach for setting up, solving and solution analysis of computer aided Molecular Design (CAMD) problems is presented. The approach differs from previous work within the field of CAMD since it also addresses the need for a computer aided problem formulation and result analysis. The problem formulation step incorporates a knowledge base for the identification and setup of the Design criteria. Candidate compounds are identified using a multi-level generate and test CAMD solution algorithm capable of Designing molecules having a high level of Molecular detail. A post solution step for result analysis and verification is included in the methodology.
Jaechang Lim - One of the best experts on this subject based on the ideXlab platform.
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scaffold based Molecular Design with a graph generative model
Chemical Science, 2020Co-Authors: Jaechang Lim, Sangyeon Hwang, Seungsu Kim, Seokhyun Moon, Woo Youn KimAbstract:Searching for new molecules in areas like drug discovery often starts from the core structures of known molecules. Such a method has called for a strategy of Designing derivative compounds retaining a particular scaffold as a substructure. On this account, our present work proposes a graph generative model that targets its use in scaffold-based Molecular Design. Our model accepts a Molecular scaffold as input and extends it by sequentially adding atoms and bonds. The generated molecules are then guaranteed to contain the scaffold with certainty, and their properties can be controlled by conditioning the generation process on desired properties. The learned rule of extending molecules can well generalize to arbitrary kinds of scaffolds, including those unseen during learning. In the conditional generation of molecules, our model can simultaneously control multiple chemical properties despite the search space constrained by fixing the substructure. As a demonstration, we applied our model to Designing inhibitors of the epidermal growth factor receptor and show that our model can employ a simple semi-supervised extension to broaden its applicability to situations where only a small amount of data is available.
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scaffold based Molecular Design using graph generative model
Chemical Science, 2020Co-Authors: Jaechang Lim, Sangyeon Hwang, Seungsu Kim, Seokhyun Moon, Woo Youn KimAbstract:Searching for new molecules in areas like drug discovery often starts from the core structures of known molecules. Such a method has called for a strategy of Designing derivative compounds retaining a particular scaffold as a substructure. On this account, our present work proposes a graph generative model that targets its use in scaffold-based Molecular Design. Our model accepts a Molecular scaffold as input and extends it by sequentially adding atoms and bonds. The generated molecules are then guaranteed to contain the scaffold with certainty, and their properties can be controlled by conditioning the generation process on desired properties. The learned rule of extending molecules can well generalize to arbitrary kinds of scaffolds, including those unseen during learning. In the conditional generation of molecules, our model can simultaneously control multiple chemical properties despite the search space constrained by fixing the substructure. As a demonstration, we applied our model to Designing inhibitors of the epidermal growth factor receptor and show that our model can employ a simple semi-supervised extension to broaden its applicability to situations where only a small amount of data is available.
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Molecular generative model based on conditional variational autoencoder for de novo Molecular Design
Journal of Cheminformatics, 2018Co-Authors: Jaechang Lim, Seongok Ryu, Jinwoo Kim, Woo Youn KimAbstract:We propose a Molecular generative model based on the conditional variational autoencoder for de novo Molecular Design. It is specialized to control multiple Molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules with five target properties. We were also able to adjust a single property without changing the others and to manipulate it beyond the range of the dataset.
Athanasios I. Papadopoulos - One of the best experts on this subject based on the ideXlab platform.
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An approach for simultaneous computer-aided Molecular Design with holistic sustainability assessment: Application to phase-change CO2 capture solvents
Computers & Chemical Engineering, 2020Co-Authors: Athanasios I. Papadopoulos, Gulnara Shavalieva, Stavros Papadokonstantakis, Panos Seferlis, Felipe A. Perdomo, Amparo Galindo, George Jackson, Claire S. AdjimanAbstract:Abstract We propose an approach for the simultaneous consideration of a holistic sustainability assessment framework in computer-aided Molecular Design (CAMD). The framework supports the assessment of life cycle (LCA) and safety, hazard and environmental (EHS) impacts from cradle-to-gate of chemicals Designed through CAMD. It enables the calculation of a total of 11 sustainability-related indicators, aggregating several impact categories. A lack of models and data gaps in property prediction are addressed through a data mining approach which deploys on-line similarity assessment against existing molecules. The LCA and EHS assessment are conducted simultaneously with CAMD or after CAMD to assess the Designed solvents. A case study is presented on the Design of phase-change solvents for chemisorption-based post-combustion CO2 capture. The proposed approach identifies verifiably useful phase-change solvents that exhibit favourable performance trade-offs compared to a reference CO2 capture solvent. The on-line use in CAMD of sustainability criteria favours the Design of hydroxyl-containing solvents.
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A Framework for the Integration of Holistic Sustainability Assessment in Computer-Aided Molecular Design
Computer Aided Chemical Engineering, 2019Co-Authors: Athanasios I. Papadopoulos, Gulnara Shavalieva, Stavros Papadokonstantakis, Panos SeferlisAbstract:Abstract We propose the integration of a holistic sustainability assessment framework in computer-aided Molecular Design (CAMD). The framework enables the assessment of life cycle (LCA) and safety, hazard and environmental (SHE) impacts from cradle-to-gate of chemicals Designed through CAMD. It enables the calculation of an overall of 14 sustainability-related indices, with some of them aggregating several impact categories. Lack of models and data gaps in property prediction are addressed systematically through a data mining approach which exploits on-line similarity assessment with existing molecules for which data exist or can be predicted. The framework is implemented both simultaneously with CAMD or after CAMD to assess the Designed solvents. A case study is presented on the Design of phase-change solvents for chemisorption-based post-combustion CO2 capture. Results indicate that the proposed approach enables the identification of verifiably useful phase-change solvents which exhibit favourable performance trade-offs compared to a reference CO2 capture solvent.
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toward optimum working fluid mixtures for organic rankine cycles using Molecular Design and sensitivity analysis
Industrial & Engineering Chemistry Research, 2013Co-Authors: Athanasios I. Papadopoulos, Panos Seferlis, Mirko Stijepovic, Patrick Linke, Spyros VoutetakisAbstract:This work presents a Computer-Aided Molecular Design (CAMD) method for the synthesis and selection of binary working fluid mixtures used in Organic Rankine Cycles (ORC). The method consists of two stages, initially seeking optimum mixture performance targets by Designing molecules acting as the first component of the binaries. The identified targets are subsequently approached by Designing the required matching molecules and selecting the optimum mixture concentration. A multiobjective formulation of the CAMD-optimization problem enables the identification of numerous mixture candidates, evaluated using an ORC process model in the course of Molecular mixture Design. A nonlinear sensitivity analysis method is employed to address model-related uncertainties in the mixture selection procedure. The proposed approach remains generic and independent of the considered mixture Design application. Mixtures of high performance are identified simultaneously with their sensitivity characteristics regardless of the em...
