Stereoisomers Are Not Machine Learning's Best Friends.

This study addresses the challenge of accurately identifying stereoisomers in cheminformatics, which originates from our objective to apply machine learning to predict the association constant between cyclodextrin and a guest. Identifying stereoisomers is indeed crucial for machine learning applications. Current tools offer various molecular descriptors, including their textual representation as Isomeric SMILES that can distinguish stereoisomers. However, such representation is text-based and does not have a fixed size, so a conversion is needed to make it usable to machine learning approaches. Word embedding techniques can be used to solve this problem. Mol2vec, a word embedding approach for molecules, offers such a conversion. Unfortunately, it cannot distinguish between stereoisomers due to its inability to capture the spatial configuration of molecular structures. This study proposes several approaches that use word embedding techniques to handle molecular discrimination using stereochemical information on molecules or considering Isomeric SMILES notation as a text in Natural Language Processing. Our aim is to generate a distinct vector for each unique molecule, correctly identifying stereoisomer information in cheminformatics. The proposed approaches are then compared to our original machine learning task: predicting the association constant between cyclodextrin and a guest molecule.

Establishing a national research software award.

Software development has become an integral part of the scholarly ecosystem, spanning all fields and disciplines. To support the sharing and creation of knowledge in line with open science principles, and particularly to enable the reproducibility of research results, it is crucial to make the source code of research software available, allowing for modification, reuse, and distribution. Recognizing the significance of open-source software contributions in academia, the second French Plan for Open Science, announced by the Minister of Higher Education and Research in 2021, introduced a National Award to promote open-source research software. This award serves multiple objectives: firstly, to highlight the software projects and teams that have devoted time and effort to develop outstanding research software, sometimes for decades, and often with little recognition; secondly, to draw attention to the importance of software as a valuable research output and to inspire new generations of researchers to follow and learn from these examples. We present here an in-depth analysis of the design and implementation of this unique initiative. As a national award established explicitly to foster Open Science practices by the French Minister of Research, it faced the intricate challenge of fairly evaluating open research software across all fields, striving for inclusivity across domains, applications, and participants. We provide a comprehensive report on the results of the first edition, which received 129 high-quality submissions. Additionally, we emphasize the impact of this initiative on the open science landscape, promoting software as a valuable research outcome, on par with publications.

Software is crucial for modern research. For the goals of open science, reproducibility, and wider reuse, sharing software source code and acknowledging software development are essential. In France, in 2021, the Minister of Higher Education and Research introduced the National Plan for Open Science. The plan highlights the role of open-source software in academia and aims to give software the same recognition as publications and data. A part of the plan is the introduction of a National Award to recognize open-source research software contributions. This award acknowledges software projects and their teams, which have often worked without much recognition. It also emphasizes the importance of software as a research output, hoping to inspire future researchers. This article examines the award's design and implementation. It addresses the challenges of assessing open research software from different research fields. In the first edition of the award, there were 129 high-quality submissions, indicating the award's potential to shift perspectives on software's role in open science, aligning it with the importance of academic publications. Through a detailed account of our experiences and the insights gained, we aim to provide a reference for other countries or institutions considering to establish similar recognitions.