Abstract: Population based exhaustive replacement method(s) (PERM) for optimal variables selection and generation of regression models, to overcome conventional approaches, thereof is described herein. PERM initializes population based on one or more criteria, wherein one or more paths for variables/descriptors 1 to r for replacement with remaining descriptors wherein the one or more paths are updated based on relative error associated with each variable. For each combination of descriptors of r size, inter correlation of the descriptors are verified and predictive models are built. Subsets of variable with higher predictive ability are selected for substitution of initial population to obtain an updated population on which a replacement method is performed to obtain optimal set of variables. One or more variables of optimal set are randomly replaced, and perturbation can be performed on the top best subsets to converge at global optimum.

Abstract: Lack of safety and efficacy are the two major unwanted biological responses that play as critical bottlenecks for the success of drug candidates in drug discovery and development. Conventional systems and methods involve ineffective exploration and use of chemical information space and thereby, may fail to address safety and efficacy issues. Embodiments of the present disclosure provides an effective solution to the above bottle-necks with the effective exploration/search of chemical information space using effective statistical techniques that yield meaningful chemical information comprising relevant descriptors, fingerprints, fragments, optimized set of structural images, and the like. Further, it provides robust predictive models for the biological response, example renal toxicity using the selected chemical information in an automated manner for a given experimental data and alerts/rules that can be successfully employed to address failures of drug candidates during discovery and development.

Abstract: Systems and methods include initializing a trainees population (TP), calculating an objective function (OF) of the TP to identify a trainer. A teaching pool is created using variables of each trainee and the identified trainer, and unique variables are added to obtain an updated teaching pool (UTP), a search is performed in parallel on UTPs to obtain ‘m’ subset of variables and OFs. OFs of ‘m’ subset are compared with OFs of the trainee's and variables of a first trainee in each thread are updated accordingly. In parallel, an updated learning pool (ULP) is created for selected trainee and the trainees, by adding unique variables to obtain ‘n’ subset which are compared with objective functions of selected trainee and the trainees and variables of a second trainee are updated accordingly. These steps are iteratively performed to obtain an optimal subset of variables that is selected for teaching and learning phase.

Abstract: Predictive regression models are widely used in different domains such as life sciences, healthcare, pharma etc. and variable selection, is employed as one of the key steps. Variable selection can be performed using random or exhaustive search techniques. Unlike a random approach, the exhaustive search approach, evaluates each possible combination and consequently, is a computationally hard problem, thus limiting its applications. The embodiments of the present disclosure perform i) parallelization and optimization of critical time consuming steps of the technique, Variable Selection and Modeling based on the Prediction (VSMP) ii) its applications for the generation of the best possible predictive models using input dataset (e.g., Blood Brain Barrier Permeation data) and iii) business impact of predictive models that are requires the selection of larger number of variables.

Abstract: Developability of a drug candidate is decided based on the Pharmacokinetic (PK) and Pharmacodynamic (PD) parameters of the drug candidate under investigation. The approaches known as of date do not always guarantee good initial estimates of all the PK-PD parameters of interest. In the present invention, a computer based solution based on hybrid modified league championship algorithm (HMLCA) is described to produce robust and optimal parameter values PK/PD parameters with minimal human intervention. Embodiments of the present disclosure generate optimized set of pharmacokinetic-pharmacodynamic parameter values by a) performing crossover technique that result in better formation and b) addition and removal of good and poor solutions respectively after a time interval to avoid unnecessary computation.

Abstract: Lack of safety and efficacy are the two major unwanted biological responses that play as critical bottlenecks for the success of drug candidates in drug discovery and development. Conventional systems and methods involve ineffective exploration and use of chemical information space and thereby, may fail to address safety and efficacy issues. Embodiments of the present disclosure provides an effective solution to the above bottlenecks with the effective exploration/search of chemical information space using effective statistical techniques that yield meaningful chemical information comprising relevant descriptors, fingerprints, fragments, optimized set of structural images, and the like. Further, it provides robust predictive models for the biological response, example renal toxicity using the selected chemical information in an automated manner for a given experimental data and alerts/rules that can be successfully employed to address failures of drug candidates during discovery and development.

Abstract: Systems and methods include initializing a trainees population (TP), calculating an objective function (OF) of the TP to identify a trainer. A teaching pool is created using variables of each trainee and the identified trainer, and unique variables are added to obtain an updated teaching pool (UTP). Search is performed on the UTP to obtain ‘m’ subset of variables and OFs. The OFs of ‘m’ subset are compared with OFs of the trainer's and each trainee's variable and one of the trainer or each trainee are updated accordingly. An updated learning pool (ULP) is created for selected trainee and the trainees, by adding unique variables to obtain ‘n’ subset. The OF of ‘n’ subset are compared with objective functions of selected trainee and the trainees and variables are updated accordingly. These steps are iteratively performed to obtain an optimal subset of variables that is selected for teaching and learning phase.

Abstract: Systems and methods include initializing a trainees population (TP), calculating an objective function (OF) of the TP to identify a trainer. A teaching pool is created using variables of each trainee and the identified trainer, and unique variables are added to obtain an updated teaching pool (UTP), a search is performed in parallel on UTPs to obtain ‘m’ subset of variables and OFs. OFs of ‘m’ subset are compared with OFs of the trainee's and variables of a first trainee in each thread are updated accordingly. In parallel, an updated learning pool (ULP) is created for selected trainee and the trainees, by adding unique variables to obtain ‘n’ subset which are compared with objective functions of selected trainee and the trainees and variables of a second trainee are updated accordingly. These steps are iteratively performed to obtain an optimal subset of variables that is selected for teaching and learning phase.

Abstract: Systems and methods include initializing a trainees population (TP), calculating an objective function (OF) of the TP to identify a trainer. A teaching pool is created using variables of each trainee and the identified trainer, and unique variables are added to obtain an updated teaching pool (UTP). Search is performed on the UTP to obtain ‘m’ subset of variables and OFs. The OFs of ‘m’ subset are compared with OFs of the trainer's and each trainee's variable and one of the trainer or each trainee are updated accordingly. An updated learning pool (ULP) is created for selected trainee and the trainees, by adding unique variables to obtain ‘n’ subset. The OF of ‘n’ subset are compared with objective functions of selected trainee and the trainees and variables are updated accordingly. These steps are iteratively performed to obtain an optimal subset of variables that is selected for teaching and learning phase.

Abstract: Predictive regression models are widely used in different domains such as life sciences, healthcare, pharma etc. and variable selection, is employed as one of the key steps. Variable selection can be performed using random or exhaustive search techniques. Unlike a random approach, the exhaustive search approach, evaluates each possible combination and consequently, is a computationally hard problem, thus limiting its applications. The embodiments of the present disclosure perform i) parallelization and optimization of critical time consuming steps of the technique, Variable Selection and Modeling based on the Prediction (VSMP) ii) its applications for the generation of the best possible predictive models using input dataset (e.g., Blood Brain Barrier Permeation data) and iii) business impact of predictive models that are requires the selection of larger number of variables.