Computer Aided Molecular Design (CAMD) has been used to supplement and guide experimental efforts... more Computer Aided Molecular Design (CAMD) has been used to supplement and guide experimental efforts in various fields. Currently, highly predictive models can be developed as a result of improved computing hardware along with novel methodologies for model development. Decreasing dependency on experimental efforts can also reduce the environmental footprint of companies involved in product design and development. Though being a well-practiced process, crystallization is comparatively a much less studied separation unit operation. Not much is known about the variables that affect quality and usefulness of the end product gained from crystallization. Studies suggest that the interactions between solute and solvent are quite difficult to quantify and large variance can be noticed with every solute-solvent combination. Pharmaceutical companies are widely known for using crystallization operations to develop their end product. Additionally, crystal morphology has a significant impact on how...
Accurate prediction of postoperative complications can inform shared decisions between patients a... more Accurate prediction of postoperative complications can inform shared decisions between patients and surgeons regarding the appropriateness of surgery, preoperative risk-reduction strategies, and postoperative resource use. Traditional predictive analytic tools are hindered by suboptimal performance and usability. We hypothesized that novel deep learning techniques would outperform logistic regression models in predicting postoperative complications. In a single-center longitudinal cohort of 43,943 adult patients undergoing 52,529 major inpatient surgeries, deep learning yielded greater discrimination than logistic regression for all nine complications. Predictive performance was strongest when leveraging the full spectrum of preoperative and intraoperative physiologic time-series electronic health record data. A single multi-task deep learning model yielded greater performance than separate models trained on individual complications. Integrated gradients interpretability mechanisms ...
Abstract The design of molecular solvents has garnered significant interest because solvents have... more Abstract The design of molecular solvents has garnered significant interest because solvents have been shown to influence the rate of chemical product generation in a reaction. In order to quantitatively understand the influence of solvent structure on the rate of the reaction, models are needed that capture this influence, in addition to that of the reactants’ structure, on the rate constant. A quantitative structure-property relationship (QSPR) for the Diels-Alder reaction was recently developed using a hybrid genetic algorithm-decision tree (GA-DT) approach. However, there is still scope for improvement in the performance of the QSPR. In an attempt to further improve upon the performance of the aforementioned QSPR, we have assessed various tree based ensemble machine learning regression methods for prediction of rate constant (modeled using connectivity indices) of Diels-Alder reaction. The assessed methods are random forest regression, gradient boosted regression trees, regularized random forest regression and extremely randomized trees. The evaluation was carried out in terms of the R2 and Q2 values. Extremely randomized trees were found to provide the highest R2 value of 0.91 while random forests provided the highest Q2 value of 0.76.
BACKGROUND Postoperative acute kidney injury is common after major vascular surgery and is associ... more BACKGROUND Postoperative acute kidney injury is common after major vascular surgery and is associated with increased morbidity, mortality, and cost. High-performance risk stratification using a machine learning model can inform strategies that mitigate harm and optimize resource use. It is hypothesized that incorporating intraoperative data would improve machine learning model accuracy, discrimination, and precision in predicting acute kidney injury among patients undergoing major vascular surgery. METHODS A single-center retrospective cohort of 1,531 adult patients who underwent nonemergency major vascular surgery, including open aortic, endovascular aortic, and lower extremity bypass procedures, was evaluated. The validated, automated MySurgeryRisk analytics platform used electronic health record data to forecast patient-level probabilistic risk scores for postoperative acute kidney injury using random forest models with preoperative data alone and perioperative data (preoperative plus intraoperative). The MySurgeryRisk predictions were compared with each other as well as with the American Society of Anesthesiologists physical status classification. RESULTS Machine learning models using perioperative data had greater accuracy, discrimination, and precision than models using either preoperative data alone or the American Society of Anesthesiologists physical status classification (accuracy: 0.70 vs 0.64 vs 0.62, area under the receiver operating characteristics curve: 0.77 vs 0.68 vs 0.61, area under the precision-recall curve: 0.70 vs 0.58 vs 0.48). CONCLUSION In predicting acute kidney injury after major vascular surgery, machine learning approaches that incorporate dynamic intraoperative data had greater accuracy, discrimination, and precision than models using either preoperative data alone or the American Society of Anesthesiologists physical status classification. Machine learning methods have the potential for real-time identification of high-risk patients who may benefit from personalized risk-reduction strategies.
Abstract Major advancements in the field of machine learning and readily available inexpensive co... more Abstract Major advancements in the field of machine learning and readily available inexpensive computational power, QSPRs (quantitative structure property relationships) are increasingly being viewed, by the scientific community, as reliable tools that can provide accurate property prediction. Additionally, QSPRs offer advantages of experimental cost reduction and reduction in chemical footprint associated with experiments. Treatment of cancerous tumors has become a global focus due to the heightened prevalence of such tumors in humans, both young and old. Apart from surgery, the most commonly used treatment is chemotherapy. As there are many long-term side effects of chemotherapy such as organ damage, fatigue, hair loss and tooth loss, researchers are devoting much attention to the search of treatments with fewer side effects. So far, no effective solution has emerged which can be reported as an alternative to chemotherapy. In a recent study, thirty one (31) 9-anilinoacridines were synthesized and evaluated for their antitumor activity. The association constant, K , was utilized as a key determining factor to evaluate the DNA drug binding affinity. 9-anilinoacridines show great promise as antitumor agents. In order to help reduce the experimental effort of K value determination and to assist in the design of 9-anilinoacridines, in this work, we developed a QSPR to predict K . In order to develop the QSPR, all the structures were drawn and optimized using the Avogadro software and converted to mol files. The Dragon 6 software was then used to calculate the values of descriptors using the generated mol files. The descriptors were then used to develop the model using GA (genetic algorithm) and CorrLASSO (correlation-based adaptive least absolute shrinkage and selection operator). The CorrLASSO in combination with GA helped generate a model with superior prediction as compared with the combination of GA and LASSO (least absolute shrinkage and selection operator) and GA-MLR (genetic algorithm-multiple linear regression). In our work, R 2 , Q 2 and MSE (mean squared error) calculations have been performed to assess model performance and data fitness.
Patients and physicians make essential decisions regarding diagnostic and therapeutic interventio... more Patients and physicians make essential decisions regarding diagnostic and therapeutic interventions. These actions should be performed or deferred under time constraints and uncertainty regarding patients' diagnoses and predicted response to treatment. This may lead to cognitive and judgment errors. Reinforcement learning is a subfield of machine learning that identifies a sequence of actions to increase the probability of achieving a predetermined goal. Reinforcement learning has the potential to assist in surgical decision making by recommending actions at predefined intervals and its ability to utilize complex input data, including text, image, and temporal data, in the decision-making process. The algorithm mimics a human trial-and-error learning process to calculate optimum recommendation policies. The article provides insight regarding challenges in the development and application of reinforcement learning in the medical field, with an emphasis on surgical decision making. The review focuses on challenges in formulating reward function describing the ultimate goal and determination of patient states derived from electronic health records, along with the lack of resources to simulate the potential benefits of suggested actions in response to changing physiological states during and after surgery. Although clinical implementation would require secure, interoperable, livestreaming electronic health record data for use by virtual model, development and validation of personalized reinforcement learning models in surgery can contribute to improving care by helping patients and clinicians make better decisions.
Abstract Developing a QSPR model, which not only captures the influence of reactant structures bu... more Abstract Developing a QSPR model, which not only captures the influence of reactant structures but also the solvent effect on reaction rate, is of significance. Such QSPR models will serve as a prerequisite for the simultaneous computer-aided molecular design (CAMD) of reactants, products and solvents. They will also be useful in predicting the rate constant without entirely relying on experiments. To develop such a QSPR, recently, Datta et al. (2017) used the Diels-Alder reaction as a case study. Their model displayed great promise, but there is scope for improvement in the model's prediction metrics. In our work, we improve upon their model by introducing non-linearity. This is achieved using multi-gene genetic programming (MGGP). In our methodology, a combination of genetic algorithm (GA) and directed trees was used to develop a branched version of chromosomes, allowing increased possibility of generation of models with high prediction metrics. In our work, prior to model development through MGGP, principal component analysis (PCA) was conducted. Lastly, models were evaluated based on metrics such as R2, Q2, and RMSE.
Abstract In recent years, Computer-Aided Molecular Design (CAMD) has been extensively used for de... more Abstract In recent years, Computer-Aided Molecular Design (CAMD) has been extensively used for defining and designing reactions at their maximal potential. In all of these contributions, either the structures of reactants/products have been considered to be unchanging or the solvent structure. Developing a QSPR model which not only captures the influence of reactant structures but also the solvent effect on reaction rate, is essential. Since the structures of reactants and products are related, such QSPR models will serve as a prerequisite for the simultaneous CAMD of reactants, products and solvents. They will also provide a useful tool for predicting the rate constant without relying on experiments. To develop such a QSPR, in our work, the Diels-Alder reaction with different sets of reactants and solvents was investigated. Connectivity indices were used to represent the structures of the members of each set. Principal Component Analysis (PCA) was applied to identify principal components (PCs) corresponding to the structures of reactants and solvent of each set. Linear models expressed in terms of PCs were then generated using a Decision Tree (DT) algorithm such that the R 2 value was maximized. These models formed the initial population on which the GA performed operations such as crossover and mutation to obtain model(s) with best rate constant prediction. Thus, the novelty of our approach is that after feature extraction using PCA, a DT algorithm generates an ensemble of linear models, which through the GA is transformed into a model with best fit. Our approach required much lesser generations to provide a model with highest R 2 ext value as compared to the case where the DT did not initialize the population of models.
Abstract In the United States, cancer is the second leading cause of death. Worldwide too, cancer... more Abstract In the United States, cancer is the second leading cause of death. Worldwide too, cancer is a major health problem. Hence, treatment of cancerous tumors remains a matter of very high concern. Apart from surgical treatment, the most commonly employed treatment is chemotherapy. But, due to long-term side effects such as organ damage and loss of teeth, doctors and patients are interested in treatments with reduced side effects. So far, a reasonably acceptable alternative to chemotherapy has not emerged. Recently, 9-anilinoacridines were evaluated as potential antitumor agents due to their enhanced tendency of DNA binding. For an initial evaluation of the drug performance, the association constant, K, is considered to be the key DNA drug binding property. In our work, to reduce experimental efforts and the associated chemical footprint, we develop a QSPR to model K. In our work, to model K, we utilized descriptors requiring representation of molecular structures in two dimensions or less. To establish a relationship between the descriptors and K, we have developed a correlation based adaptive LASSO algorithm (CorrLASSO). CorrLASSO, like LASSO (least absolute shrinkage and selection operator), incorporates feature selection as part of the learning procedure. Also, it is useful for dealing with high-dimensional data. As an improvement, CorrLASSO evaluates correlation between descriptors/features and the dependent property to generate a model with high performance metrics. In our work, R2, Q2 and MSE (mean square error) were utilized as performance metrics.
Abstract In recent years, Computer-Aided Molecular Design (CAMD) has been extensively used for de... more Abstract In recent years, Computer-Aided Molecular Design (CAMD) has been extensively used for defining and designing reactions at their maximal potential. In all these contributions, either the reactants/products were considered constant or the solvents. Developing a QSPR model which not only captures the influence of reactant structures but also the solvent effect on reaction rate, is essential. Since the structures of reactants and products are related, such QSPR models will serve as a prerequisite for the simultaneous CAMD of reactants, products and solvents. They will also provide a useful tool for predicting the rate constant without relying on experiments. To develop such a QSPR, in our work, the Diels-Alder reaction with different sets of reactants and solvents was investigated. Connectivity indices were used to represent the structures of the members of each set. Principal Component Analysis (PCA) was applied to identify the principal components (PCs) of each set for further use in model development. These PCs were then used to develop a linear model that best predicts the reaction rates in our study. In this paper, Genetic Algorithm (GA) has been modified using the Decision Tree (DT) algorithm for increased efficiency. Inclusion of DT in GA ensures an initial generation of meaningful combination of descriptors. This set gets further improved in every step of crossover and mutation with applied constraints. Only improvement of generations is accepted due to these constraints. Finally, Multiple Linear Regression (MLR) relates the chosen descriptors with the property under study. The model undergoes thorough internal and external validation to ensure that a best fit model can be found in minimum steps possible.
Abstract This contribution outlines the development of a quantitative structure-property relation... more Abstract This contribution outlines the development of a quantitative structure-property relationship (QSPR) that relates solvent structure to the morphology of ibuprofen crystals grown within that solvent. Morphology can be quantified by aspect ratio, and ibuprofen aspect ratio data was obtained for crystals grown in 16 different organic solvents. A combination of 2D and 3D molecular descriptors are calculated to provide a quantitative representation of the geometry optimized solvent molecules. Empirical force fields were used to estimate the three-dimensional structure of the solvent molecules and three different force fields are implemented while their effect on the developed models is analyzed. The descriptor data matrix, containing a multitude of descriptor types, is reduced in size, using Bayesian Information Criterion (BIC) methods and also Principal Component Analysis (PCA), for regression into linear models through Principal Component Regression. The predictive capabilities of these models were also analyzed through means of internal and external validation methods.
Computer Aided Molecular Design (CAMD) has been used to supplement and guide experimental efforts... more Computer Aided Molecular Design (CAMD) has been used to supplement and guide experimental efforts in various fields. Currently, highly predictive models can be developed as a result of improved computing hardware along with novel methodologies for model development. Decreasing dependency on experimental efforts can also reduce the environmental footprint of companies involved in product design and development. Though being a well-practiced process, crystallization is comparatively a much less studied separation unit operation. Not much is known about the variables that affect quality and usefulness of the end product gained from crystallization. Studies suggest that the interactions between solute and solvent are quite difficult to quantify and large variance can be noticed with every solute-solvent combination. Pharmaceutical companies are widely known for using crystallization operations to develop their end product. Additionally, crystal morphology has a significant impact on how...
Accurate prediction of postoperative complications can inform shared decisions between patients a... more Accurate prediction of postoperative complications can inform shared decisions between patients and surgeons regarding the appropriateness of surgery, preoperative risk-reduction strategies, and postoperative resource use. Traditional predictive analytic tools are hindered by suboptimal performance and usability. We hypothesized that novel deep learning techniques would outperform logistic regression models in predicting postoperative complications. In a single-center longitudinal cohort of 43,943 adult patients undergoing 52,529 major inpatient surgeries, deep learning yielded greater discrimination than logistic regression for all nine complications. Predictive performance was strongest when leveraging the full spectrum of preoperative and intraoperative physiologic time-series electronic health record data. A single multi-task deep learning model yielded greater performance than separate models trained on individual complications. Integrated gradients interpretability mechanisms ...
Abstract The design of molecular solvents has garnered significant interest because solvents have... more Abstract The design of molecular solvents has garnered significant interest because solvents have been shown to influence the rate of chemical product generation in a reaction. In order to quantitatively understand the influence of solvent structure on the rate of the reaction, models are needed that capture this influence, in addition to that of the reactants’ structure, on the rate constant. A quantitative structure-property relationship (QSPR) for the Diels-Alder reaction was recently developed using a hybrid genetic algorithm-decision tree (GA-DT) approach. However, there is still scope for improvement in the performance of the QSPR. In an attempt to further improve upon the performance of the aforementioned QSPR, we have assessed various tree based ensemble machine learning regression methods for prediction of rate constant (modeled using connectivity indices) of Diels-Alder reaction. The assessed methods are random forest regression, gradient boosted regression trees, regularized random forest regression and extremely randomized trees. The evaluation was carried out in terms of the R2 and Q2 values. Extremely randomized trees were found to provide the highest R2 value of 0.91 while random forests provided the highest Q2 value of 0.76.
BACKGROUND Postoperative acute kidney injury is common after major vascular surgery and is associ... more BACKGROUND Postoperative acute kidney injury is common after major vascular surgery and is associated with increased morbidity, mortality, and cost. High-performance risk stratification using a machine learning model can inform strategies that mitigate harm and optimize resource use. It is hypothesized that incorporating intraoperative data would improve machine learning model accuracy, discrimination, and precision in predicting acute kidney injury among patients undergoing major vascular surgery. METHODS A single-center retrospective cohort of 1,531 adult patients who underwent nonemergency major vascular surgery, including open aortic, endovascular aortic, and lower extremity bypass procedures, was evaluated. The validated, automated MySurgeryRisk analytics platform used electronic health record data to forecast patient-level probabilistic risk scores for postoperative acute kidney injury using random forest models with preoperative data alone and perioperative data (preoperative plus intraoperative). The MySurgeryRisk predictions were compared with each other as well as with the American Society of Anesthesiologists physical status classification. RESULTS Machine learning models using perioperative data had greater accuracy, discrimination, and precision than models using either preoperative data alone or the American Society of Anesthesiologists physical status classification (accuracy: 0.70 vs 0.64 vs 0.62, area under the receiver operating characteristics curve: 0.77 vs 0.68 vs 0.61, area under the precision-recall curve: 0.70 vs 0.58 vs 0.48). CONCLUSION In predicting acute kidney injury after major vascular surgery, machine learning approaches that incorporate dynamic intraoperative data had greater accuracy, discrimination, and precision than models using either preoperative data alone or the American Society of Anesthesiologists physical status classification. Machine learning methods have the potential for real-time identification of high-risk patients who may benefit from personalized risk-reduction strategies.
Abstract Major advancements in the field of machine learning and readily available inexpensive co... more Abstract Major advancements in the field of machine learning and readily available inexpensive computational power, QSPRs (quantitative structure property relationships) are increasingly being viewed, by the scientific community, as reliable tools that can provide accurate property prediction. Additionally, QSPRs offer advantages of experimental cost reduction and reduction in chemical footprint associated with experiments. Treatment of cancerous tumors has become a global focus due to the heightened prevalence of such tumors in humans, both young and old. Apart from surgery, the most commonly used treatment is chemotherapy. As there are many long-term side effects of chemotherapy such as organ damage, fatigue, hair loss and tooth loss, researchers are devoting much attention to the search of treatments with fewer side effects. So far, no effective solution has emerged which can be reported as an alternative to chemotherapy. In a recent study, thirty one (31) 9-anilinoacridines were synthesized and evaluated for their antitumor activity. The association constant, K , was utilized as a key determining factor to evaluate the DNA drug binding affinity. 9-anilinoacridines show great promise as antitumor agents. In order to help reduce the experimental effort of K value determination and to assist in the design of 9-anilinoacridines, in this work, we developed a QSPR to predict K . In order to develop the QSPR, all the structures were drawn and optimized using the Avogadro software and converted to mol files. The Dragon 6 software was then used to calculate the values of descriptors using the generated mol files. The descriptors were then used to develop the model using GA (genetic algorithm) and CorrLASSO (correlation-based adaptive least absolute shrinkage and selection operator). The CorrLASSO in combination with GA helped generate a model with superior prediction as compared with the combination of GA and LASSO (least absolute shrinkage and selection operator) and GA-MLR (genetic algorithm-multiple linear regression). In our work, R 2 , Q 2 and MSE (mean squared error) calculations have been performed to assess model performance and data fitness.
Patients and physicians make essential decisions regarding diagnostic and therapeutic interventio... more Patients and physicians make essential decisions regarding diagnostic and therapeutic interventions. These actions should be performed or deferred under time constraints and uncertainty regarding patients' diagnoses and predicted response to treatment. This may lead to cognitive and judgment errors. Reinforcement learning is a subfield of machine learning that identifies a sequence of actions to increase the probability of achieving a predetermined goal. Reinforcement learning has the potential to assist in surgical decision making by recommending actions at predefined intervals and its ability to utilize complex input data, including text, image, and temporal data, in the decision-making process. The algorithm mimics a human trial-and-error learning process to calculate optimum recommendation policies. The article provides insight regarding challenges in the development and application of reinforcement learning in the medical field, with an emphasis on surgical decision making. The review focuses on challenges in formulating reward function describing the ultimate goal and determination of patient states derived from electronic health records, along with the lack of resources to simulate the potential benefits of suggested actions in response to changing physiological states during and after surgery. Although clinical implementation would require secure, interoperable, livestreaming electronic health record data for use by virtual model, development and validation of personalized reinforcement learning models in surgery can contribute to improving care by helping patients and clinicians make better decisions.
Abstract Developing a QSPR model, which not only captures the influence of reactant structures bu... more Abstract Developing a QSPR model, which not only captures the influence of reactant structures but also the solvent effect on reaction rate, is of significance. Such QSPR models will serve as a prerequisite for the simultaneous computer-aided molecular design (CAMD) of reactants, products and solvents. They will also be useful in predicting the rate constant without entirely relying on experiments. To develop such a QSPR, recently, Datta et al. (2017) used the Diels-Alder reaction as a case study. Their model displayed great promise, but there is scope for improvement in the model's prediction metrics. In our work, we improve upon their model by introducing non-linearity. This is achieved using multi-gene genetic programming (MGGP). In our methodology, a combination of genetic algorithm (GA) and directed trees was used to develop a branched version of chromosomes, allowing increased possibility of generation of models with high prediction metrics. In our work, prior to model development through MGGP, principal component analysis (PCA) was conducted. Lastly, models were evaluated based on metrics such as R2, Q2, and RMSE.
Abstract In recent years, Computer-Aided Molecular Design (CAMD) has been extensively used for de... more Abstract In recent years, Computer-Aided Molecular Design (CAMD) has been extensively used for defining and designing reactions at their maximal potential. In all of these contributions, either the structures of reactants/products have been considered to be unchanging or the solvent structure. Developing a QSPR model which not only captures the influence of reactant structures but also the solvent effect on reaction rate, is essential. Since the structures of reactants and products are related, such QSPR models will serve as a prerequisite for the simultaneous CAMD of reactants, products and solvents. They will also provide a useful tool for predicting the rate constant without relying on experiments. To develop such a QSPR, in our work, the Diels-Alder reaction with different sets of reactants and solvents was investigated. Connectivity indices were used to represent the structures of the members of each set. Principal Component Analysis (PCA) was applied to identify principal components (PCs) corresponding to the structures of reactants and solvent of each set. Linear models expressed in terms of PCs were then generated using a Decision Tree (DT) algorithm such that the R 2 value was maximized. These models formed the initial population on which the GA performed operations such as crossover and mutation to obtain model(s) with best rate constant prediction. Thus, the novelty of our approach is that after feature extraction using PCA, a DT algorithm generates an ensemble of linear models, which through the GA is transformed into a model with best fit. Our approach required much lesser generations to provide a model with highest R 2 ext value as compared to the case where the DT did not initialize the population of models.
Abstract In the United States, cancer is the second leading cause of death. Worldwide too, cancer... more Abstract In the United States, cancer is the second leading cause of death. Worldwide too, cancer is a major health problem. Hence, treatment of cancerous tumors remains a matter of very high concern. Apart from surgical treatment, the most commonly employed treatment is chemotherapy. But, due to long-term side effects such as organ damage and loss of teeth, doctors and patients are interested in treatments with reduced side effects. So far, a reasonably acceptable alternative to chemotherapy has not emerged. Recently, 9-anilinoacridines were evaluated as potential antitumor agents due to their enhanced tendency of DNA binding. For an initial evaluation of the drug performance, the association constant, K, is considered to be the key DNA drug binding property. In our work, to reduce experimental efforts and the associated chemical footprint, we develop a QSPR to model K. In our work, to model K, we utilized descriptors requiring representation of molecular structures in two dimensions or less. To establish a relationship between the descriptors and K, we have developed a correlation based adaptive LASSO algorithm (CorrLASSO). CorrLASSO, like LASSO (least absolute shrinkage and selection operator), incorporates feature selection as part of the learning procedure. Also, it is useful for dealing with high-dimensional data. As an improvement, CorrLASSO evaluates correlation between descriptors/features and the dependent property to generate a model with high performance metrics. In our work, R2, Q2 and MSE (mean square error) were utilized as performance metrics.
Abstract In recent years, Computer-Aided Molecular Design (CAMD) has been extensively used for de... more Abstract In recent years, Computer-Aided Molecular Design (CAMD) has been extensively used for defining and designing reactions at their maximal potential. In all these contributions, either the reactants/products were considered constant or the solvents. Developing a QSPR model which not only captures the influence of reactant structures but also the solvent effect on reaction rate, is essential. Since the structures of reactants and products are related, such QSPR models will serve as a prerequisite for the simultaneous CAMD of reactants, products and solvents. They will also provide a useful tool for predicting the rate constant without relying on experiments. To develop such a QSPR, in our work, the Diels-Alder reaction with different sets of reactants and solvents was investigated. Connectivity indices were used to represent the structures of the members of each set. Principal Component Analysis (PCA) was applied to identify the principal components (PCs) of each set for further use in model development. These PCs were then used to develop a linear model that best predicts the reaction rates in our study. In this paper, Genetic Algorithm (GA) has been modified using the Decision Tree (DT) algorithm for increased efficiency. Inclusion of DT in GA ensures an initial generation of meaningful combination of descriptors. This set gets further improved in every step of crossover and mutation with applied constraints. Only improvement of generations is accepted due to these constraints. Finally, Multiple Linear Regression (MLR) relates the chosen descriptors with the property under study. The model undergoes thorough internal and external validation to ensure that a best fit model can be found in minimum steps possible.
Abstract This contribution outlines the development of a quantitative structure-property relation... more Abstract This contribution outlines the development of a quantitative structure-property relationship (QSPR) that relates solvent structure to the morphology of ibuprofen crystals grown within that solvent. Morphology can be quantified by aspect ratio, and ibuprofen aspect ratio data was obtained for crystals grown in 16 different organic solvents. A combination of 2D and 3D molecular descriptors are calculated to provide a quantitative representation of the geometry optimized solvent molecules. Empirical force fields were used to estimate the three-dimensional structure of the solvent molecules and three different force fields are implemented while their effect on the developed models is analyzed. The descriptor data matrix, containing a multitude of descriptor types, is reduced in size, using Bayesian Information Criterion (BIC) methods and also Principal Component Analysis (PCA), for regression into linear models through Principal Component Regression. The predictive capabilities of these models were also analyzed through means of internal and external validation methods.
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