Helle W. van Den Maagdenberg

Integrated understanding of pharmacokinetics (PK) and pharmacodynamics (PD) is a key aspect of successful drug discovery. Yet in generative computational drug design, the focus often lies on optimizing potency. Here we integrate PK property predictions in DrugEx, a generative drug design framework and we explore the generated compounds' PD through simulations with a quantitative systems pharmacology (QSP) model. Quantitative structure-property relationship models were developed to predict molecule PK (clearance, volume of distribution and unbound fraction) and affinity for the Adenosine A_2AR receptor (A_2AR), a drug target in immuno-oncology. These models were used to score compounds in a reinforcement learning framework to generate molecules with a specific PK profile and high affinity for the A_2AR. We predicted the expected tumor growth inhibition profiles using the QSP model for selected candidate molecules with varying PK and affinity profiles. We show that optimizing affinity to the A_2AR, while minimizing or maximizing a PK property, shifts the type of molecular scaffolds that are generated. The difference in physicochemical properties of the compounds with different predicted PK parameters was found to correspond with the differences observed in the PK data set. We demonstrated the use of the QSP model by simulating the effect of a broad range of compound properties on the predicted tumor volume. In conclusion, our proposed integrated workflow incorporating affinity predictions with PKPD may provide a template for the next generation of advanced generative computational drug design.

Integrated understanding of pharmacokinetics (PK) and pharmacodynamics (PD) is a key aspect of successful drug discovery. Yet in generative computational drug design, the focus often lies on optimizing potency. Here we integrate PK property predictions in DrugEx, a generative drug design framework and we explore the generated compounds’ PD through simulations with a quantitative systems pharmacology (QSP) model. Quantitative structure-property relationship models were developed to predict molecule PK (clearance, volume of distribution and unbound fraction) and affinity for the Adenosine A2AR receptor (A2AR), a drug target in immuno-oncology. These models were used to score compounds in a reinforcement learning framework to generate molecules with a specific PK profile and high affinity for the A2AR. We predicted the expected tumor growth inhibition profiles using the QSP model for selected candidate molecules with varying PK and affinity profiles. We show that optimizing affinity to the A2AR, while minimizing or maximizing a PK property, shifts the type of molecular scaffolds that are generated. The difference in physicochemical properties of the compounds with different predicted PK parameters was found to correspond with the differences observed in the PK dataset. We demonstrated the use of the QSP model by simulating the effect of a broad range of compound properties on the predicted tumor volume. In conclusion, our proposed integrated workflow incorporating affinity predictions with PKPD may provide a template for the next generation of advanced generative computational drug design.

Building reliable and robust quantitative structure–property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred’s modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrates customized implementations in a “plug-and-play” manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred’s functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at https://github.com/CDDLeiden/QSPRpred.Scientific ContributionQSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models.

Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred's modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrate customized implementations in a "plug-and-play" manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred's functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at https://github.com/CDDLeiden/QSPRpred. Scientific Contribution QSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models.

The discovery of novel molecules with desirable properties is a classic challenge in medicinal chemistry. With the recent advancements of machine learning, there has been a surge of de novo drug design tools. However, few resources exist that are user-friendly as well as easily customizable. In this application note, we present the new versatile open-source software package DrugEx for multiobjective reinforcement learning. This package contains the consolidated and redesigned scripts from the prior DrugEx papers including multiple generator architectures, a variety of scoring tools, and multiobjective optimization methods. It has a flexible application programming interface and can readily be used via the command line interface or the graphical user interface GenUI. The DrugEx package is publicly available at https://github.com/CDDLeiden/DrugEx.

The discovery of novel molecules with desirable properties is a classic challenge in medicinal chemistry. With the recent advancements of machine learning, there has been a surge of de novo drug design tools. However, few resources exist that are both user-friendly as well as easily customisable. In this application note, we present the new versatile open-source software package DrugEx for multi-objective reinforcement learning. This package contains the consolidated and redesigned scripts from the prior DrugEx papers including multiple generator architectures and a variety of scoring tools and multi-objective optimisation methods. It has a flexible application programming interface and can readily be used via the command line interface or the graphical user interface GenUI. The DrugEx package is publicly available at https://github.com/CDDLeiden/DrugEx