Fanwang Meng

Selector is a free, open-source Python library for selecting diverse subsets from any dataset, making it a versatile tool across a wide range of application domains. Selector implements different subset sampling algorithms based on sample distance, similarity, and spatial partitioning along with metrics to quantify subset diversity. It is flexible and integrates seamlessly with popular Python libraries such as Scikit-Learn, demonstrating the interoperability of the implemented algorithms with data analysis workflows. Selector is an operating-system-agnostic, accessible, and easily extensible package designed with modern software development practices, including version control, unit testing, and continuous integration. Interactive quick-start notebooks, which are also web-accessible, provide user-friendly tutorials for all skill levels, showcasing applications in computational chemistry, drug discovery, and chemical library design. Additionally, a web interface has been developed that allows users to easily upload datasets, configure sampling settings, and run subset selection algorithms with no programming required. This work serves as the official release note for the Selector package, offering a technical overview of its features, use cases, and development practices that ensure its quality and maintainability.

Selector is a free, open-source Python library for selecting diverse subsets from any dataset, making it a versatile tool across a wide range of application domains. Selector implements different subset sampling algorithms based on sample distance, similarity, and spatial partitioning, along with metrics to quantify subset diversity. It is flexible and integrates seamlessly with popular Python libraries like Scikit-Learn , demonstrating the interoperability of the implemented algorithms with data analysis workflows. Selector is an operating-system agnostic, accessible, and easily extensible package designed with modern software development practices, including version control, unit testing, and continuous integration. Interactive quick-start notebooks, which are also web-accessible, provide user-friendly tutorials for all skill levels, showcasing applications in computational chemistry, drug discovery, and chemical library design. Additionally, a web interface has been developed that allows users to easily upload datasets, configure sampling settings, and run subset selection algorithms, with no programming required. This paper serves as the official release note for the Selector package, offering a technical overview of its features, use cases, and development practices that ensure its quality and maintainability.

Developing accurate, computationally efficient, and reliable predictive models for small molecules' blood-brain barrier (BBB) permeability is challenging due to the class imbalance often found in collections of reference data. We use resampling techniques to address class imbalance and build 24 types of machine learning models, which we developed using comprehensive hyperparameter optimizations. We evaluated our model against those from previous studies, which provides insight into optimal classification models and resampling techniques that are relevant beyond BBB permeability. In addition to classifying unknown compounds on the basis of BBB permeability, the predicted probabilities are provided to facilitate further improvements and comparative benchmarking, and to report the models' confidence in their predictions. To disseminate our findings, we developed B3clf, a highly efficient, user-friendly tool that facilitates BBB permeability prediction, which can be accessed as open-source software https://github.com/theochem/B3clf or as a web app https://huggingface.co/spaces/QCDevs/b3clf. The newly curated external dataset for BBB is hosted at https://github.com/theochem/B3DB.

AtomDB is a free and open-source Python library for accessing and manipulating neutral and charged atomic species and their promolecular properties. It serves as a computational toolset, operating on an accompanying "extended periodic table" database, with experimental and computational data covering atomic species with a wide range of charges and multiplicities. AtomDB includes facilities for computing promolecules: local promolecular properties, constructed from the corresponding atomic densities, and scalar promolecular properties, computed from the corresponding scalar atomic properties, both taking into account whether properties are extensive or intensive. AtomDB is designed to be easy to use, extend, and maintain: it follows best practices for modern software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. This article is the official release note for the AtomDB library.

Synthetic routes of a chemical library, represented by a reaction network, are used to create an operation network including constraints and precedence relations. The scheduler proposed uses the operation network to produce an optimal schedule.

Automated chemistry platforms hold the potential to enable large-scale organic synthesis campaigns, such as producing a library of compounds for biological evaluation. The efficiency of such platforms will depend on the schedule according to which the synthesis operations are executed. In this work, we study the scheduling problem for chemical library synthesis, where operations from interdependent synthetic routes are scheduled to minimize the makespan---the total duration of the synthesis campaign. We formalize this problem as a flexible job-shop scheduling problem with chemistry-relevant constraints in the form of a mixed integer linear program (MILP), which we then solve in order to design an optimized schedule. The scheduler's ability to produce valid, optimal schedules is demonstrated by 720 simulated scheduling instances for realistically accessible chemical libraries. Reductions in makespan up to 58%, with an average reduction of 20%, are observed compared to the baseline scheduling approach.

The popularity of data-driven approaches and machine learning (ML) techniques in the field of organic chemistry and its various subfields has increased the value of structured reaction data. Most data in chemistry is represented by unstructured text, and despite the vastness of the organic chemistry literature (papers, patents), manual conversion from unstructured text to structured data remains a largely manual endeavor. Software tools for this task would facilitate downstream applications such as reaction prediction and condition recommendation. In this study, we fine-tune a large language model (LLM) to extract reaction information from organic synthesis procedure text into structured data following the Open Reaction Database (ORD) schema, a comprehensive data structure designed for organic reactions. The fine-tuned model produces syntactically correct ORD records with an average accuracy of 91.25% for ORD “messages” (e.g., full compound, workups, or condition definitions) and 92.25% for individual data fields (e.g., compound identifiers, mass quantities), with the ability to recognize compound-referencing tokens and to infer reaction roles. We investigate its failure modes and evaluate performance on specific subtasks such as reaction role classification.

The popularity of data-driven approaches and machine learning (ML) techniques in the field of organic chemistry and its various subfields has increased the value of structured reaction data. Most data in chemistry is represented by unstructured text, and due to the vastness of the organic chemistry literature (papers, patents), manual conversion from unstructured text to structured data remains a largely manual endeavor. Software tools for this task would facilitate downstream applications such as reaction prediction and condition recommendation. In this study, we leverage the power of fine-tuned large language models (LLMs) to extract reaction information from organic synthesis procedure text into structured data following the Open Reaction Database (ORD) schema, a comprehensive data structure designed for organic reactions. The fine-tuned model produces syntactically correct ORD records with an average accuracy of 91.25% for ORD “messages” (e.g., full compound, workups, or condition definitions) and 92.25% for individual data fields (e.g., compound identifiers, mass quantities), with the ability to recognize compound-referencing tokens and to infer reaction roles. We investigate its failure modes and evaluate performance on specific subtasks such as reaction role classification.