Predictive deep learning models for environmental properties: the direct calculation of octanol–water partition coefficients from molecular graphs

Wang, Zihao; Yang, Su; Shen, Weifeng; Jin, Saimeng; Clark, James H.; Ren, Jingzheng; Zhang, Xiangping

doi:10.1039/c9gc01968e

Cited by 82 publications

(66 citation statements)

References 58 publications

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“…The tree-LSTM algorithm was initially proposed for tasks such as semantic relatedness of two sentences and sentiment classification 27 . It has recently been used to encode an organic molecule by converting atoms into tree nodes and bonds into tree connections 28 . The encoded pathway is represented by a latent numeric vector, which will be further processed for two tasks as discussed above, ranking pathways based the relative strategic level and clustering similar pathways. )…”

Section: Tree-structured Lstm Modelmentioning

confidence: 99%

Evaluating and clustering retrosynthesis pathways with learned strategy

Guan

Verma

et al. 2021

Chem. Sci.

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Section: Tree-structured Lstm Modelmentioning

confidence: 99%

Evaluating and clustering retrosynthesis pathways with learned strategy

Guan

Verma

et al. 2021

Chem. Sci.

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“…As proven by many studies [20][21][22][23][24][25], machine learning (ML) is an efficient and promising approach for building quantitative structure-property relationship (QSPR) models to predict various properties for chemical compounds. Until now, ML techniques have obtained wide applications and great successes in predicting IL properties, including melting point [26], CO 2 solubility [27], viscosity [28], etc.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Ionic Liquid Toxicity Prediction

2020

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In addition to proper physicochemical properties, low toxicity is also desirable when seeking suitable ionic liquids (ILs) for specific applications. In this context, machine learning (ML) models were developed to predict the IL toxicity in leukemia rat cell line (IPC-81) based on an extended experimental dataset. Following a systematic procedure including framework construction, hyper-parameter optimization, model training, and evaluation, the feedforward neural network (FNN) and support vector machine (SVM) algorithms were adopted to predict the toxicity of ILs directly from their molecular structures. Based on the ML structures optimized by the five-fold cross validation, two ML models were established and evaluated using IL structural descriptors as inputs. It was observed that both models exhibited high predictive accuracy, with the SVM model observed to be slightly better than the FNN model. For the SVM model, the determination coefficients were 0.9289 and 0.9202 for the training and test sets, respectively. The satisfactory predictive performance and generalization ability make our models useful for the computer-aided molecular design (CAMD) of environmentally friendly ILs.

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“…The candidate list for SVHC contains some solvents including nitrobenzene, o-toluidine, N-methylacetamide, N,N-dimethylformamide, furan, formamide, N,N-dimethylacetamide, N-methylpyrrolidone, 2-(m)ethoxyethanol, trichloroethylene, 1,2,3-trichloropropane, and 2,4-dinitrotoluene. prediction of octanol-water partition coefficients [36], solvation free energies [37], gas chromatographic retention indices [38]. and critical properties [39].…”

Section: Introductionmentioning

confidence: 99%

“…and critical properties [39]. Often SMILES strings are used as the input for the DNN model [36][37][38][39]. Such an approach proves useful in the screening and development of green solvents with respect to unconventional and novel compounds.…”

Section: Introductionmentioning

confidence: 99%

“…State-of-the-art AI supported quantitative structure property relationship (QSPR) modeling makes use of deep neural networks (DNN). Among others, AI supported QSPRs have been used for the prediction of octanol–water partition coefficients [ 36 ], solvation free energies [ 37 ], gas chromatographic retention indices [ 38 ]. and critical properties [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

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SUSSOL—Using Artificial Intelligence for Greener Solvent Selection and Substitution

2020

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Solvents come in many shapes and types. Looking for solvents for a specific application can be hard, and looking for green alternatives for currently used nonbenign solvents can be even harder. We describe a new methodology for solvent selection and substitution, by applying Artificial Intelligence (AI) software to cluster a database of solvents based on their physical properties. The solvents are processed by a neural network, the Self-organizing Map of Kohonen, which results in a 2D map of clusters. The resulting clusters are validated both chemically and statistically and are presented in user-friendly visualizations by the SUSSOL (Sustainable Solvents Selection and Substitution Software) software. The software helps the user in exploring the solvent space and in generating and evaluating a list of possible alternatives for a specific solvent. The alternatives are ranked based on their safety, health, and environment scores. Cases are discussed to demonstrate the possibilities of our approach and to show that it can help in the search for more sustainable and greener solvents. The SUSSOL software makes intuitive sense and in most case studies, the software confirms the findings in literature, thus providing a sound platform for selecting the most sustainable solvent candidate.

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Predictive deep learning models for environmental properties: the direct calculation of octanol–water partition coefficients from molecular graphs

Abstract: A deep learning approach coupling the Tree-LSTM network and back-propagation neural network for predicting the octanol–water partition coefficient.

Cited by 82 publications

References 58 publications

Evaluating and clustering retrosynthesis pathways with learned strategy

Evaluating and clustering retrosynthesis pathways with learned strategy

Machine Learning for Ionic Liquid Toxicity Prediction

SUSSOL—Using Artificial Intelligence for Greener Solvent Selection and Substitution

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