Open access and open source in chemistry

Combining methods to conduct a systematic review and propose a conceptual and theoretical framework in socio-environmental research

This study aims to present a combination of methods and propose robust theoretical and conceptual frameworks for solving socio-environmental issues. This proposal included the Problem, Intervention, Context, and Outcome (PICO) framework and Protocol and Reporting result with Search, Appraisal, Synthesis, and Analysis framework, and develop (PSALSAR) method through SODIP steps: (i) Systematic review and meta-analysis defining the study from guiding questions; (ii) Open-source related to software and data; (iii) Data visualization and design information; (iv) Identification of gaps, challenges and trends through automation and lexicometric analysis; and (v) Proposal of theoretical and conceptual frameworks. This proposal defines the steps as support to combine and systematize information necessary to facilitate the production of this type of document using open-access software in the visualization and design of information. All these steps are replicable and essential to propose a conceptual and theoretical framework to contribute to the construction of knowledge in socio-environmental research and to propose solutions by filling in the gaps. In summary, this combination of methods shows:•The use of SODIP steps provides robustness and efficiency in carrying out review studies, facilitating the way to propose theoretical or conceptual frameworks.•Choosing to use open-source tools is essential for better evaluation and visualization of qualitative and quantitative data in review studies.•The combination of methods and data in the systematic review (scientific, political documents. and databases) supports the proposal of robust theoretical and conceptual frameworks.

The chemfp project

The chemfp project has had four main goals: (1) promote the FPS format as a text-based exchange format for dense binary cheminformatics fingerprints, (2) develop a high-performance implementation of the BitBound algorithm that could be used as an effective baseline to benchmark new similarity search implementations, (3) experiment with funding a pure open source software project through commercial sales, and (4) publish the results and lessons learned as a guide for future implementors. The FPS format has had only minor success, though it did influence development of the FPB binary format, which is faster to load but more complex. Both are summarized. The chemfp benchmark and the no-cost/open source version of chemfp are proposed as a reference baseline to evaluate the effectiveness of other similarity search tools. They are used to evaluate the faster commercial version of chemfp, which can test 130 million 1024-bit fingerprint Tanimotos per second on a single core of a standard x86-64 server machine. When combined with the BitBound algorithm, a k = 1000 nearest-neighbor search of the 1.8 million 2048-bit Morgan fingerprints of ChEMBL 24 averages 27 ms/query. The same search of 970 million PubChem fingerprints averages 220 ms/query, making chemfp one of the fastest CPU-based similarity search implementations. Modern CPUs are fast enough that memory bandwidth and latency are now important factors. Single-threaded search uses most of the available memory bandwidth. Sorting the fingerprints by popcount improves memory coherency, which when combined with 4 OpenMP threads makes it possible to construct an N × N similarity matrix for 1 million fingerprints in about 30 min. These observations may affect the interpretation of previous publications which assumed that search was strongly CPU bound. The chemfp project funding came from selling a purely open-source software product. Several product business models were tried, but none proved sustainable. Some of the experiences are discussed, in order to contribute to the ongoing conversation on the role of open source software in cheminformatics.

Bigger data, collaborative tools and the future of predictive drug discovery

Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas.

Going beyond the textbook: The need to integrate open access primary literature into the Chemistry curriculum

Unrestricted, open access to scholarly scientific literature provides an opportunity for chemistry educators to go beyond the textbook, introducing students to the real work of scientists. Despite the best efforts of textbook authors to provide information about recent research results, textbooks are not a substitute for learning to use the primary literature. Chemical educators can use open access articles to develop research-related skills, to foster curiosity, and to cultivate the next generation of scientists. It is becoming increasingly important for chemical educators to teach undergraduates how online journals are changing the nature of chemical research. Some institutions can not afford online subscription costs, and open access journals can be an important resource to provide practical experience. Open access publications eliminate the barriers to the central work of scientists providing chemistry educators (whether at well-endowed or economically limited colleges) with the key resources for enhancing student learning through current, relevant research.

Open Source Tools for Read-Across and Category Formation

In this chapter, the requirements and challenges for computational tools to support category formation and read-across are summarised. A brief overview of the open source, open data and open standards approaches in chemoinformatics are presented. The opportunities offered by these kinds of “openness” are highlighted, with emphasis on open source applications specifically developed to address challenges posed by the REACH regulation. Structural similarity assessment is currently a common practice in forming categories and applying read-across, and in developing and validating (Q)SARs. The Toxmatch software provides several endpoint-specific similarity measures, with descriptors selected using a training set in combination with data mining methods. The Toxtree software implements several classification schemes for predicting various endpoints and relies primarily on chemical structure, metabolic pathways, physico-chemical properties and descriptors, calculated from chemical structure. In addition to making predictions for individual chemicals, Toxtree can be used to profile the toxicological hazard or mechanistic group of a set of chemicals. Ambit is anopen source software for chemoinformatics data management, which allows storage of a large number of chemical structures and toxicological data and provides a flexible means for exploration of structural and similarity spaces. Several examples of the application of read-across, initiated by an expert-defined search strategy and supported by Ambit search functionalities are described.