Listening to placebos: the contested lessons of antidepressants debates.

The goal of this paper is to explore a set of epistemological and ontological issues regarding the historical and philosophical role of placebos in the contested history of antidepressants. Starting from an account of the dual nature of the placebo as both an epistemic and a therapeutic tool, and against the background of the heated debates on the efficacy of second-generation antidepressants, I propose two related arguments. First, I argue that placebos as controls played a crucial but paradoxical role in the rise of so-called evidence-based approaches to depression.

The Impact of Interdisciplinary, Gender and Geographic Distributions on the Citation Patterns of the Journal of Chemical Information and Modeling.

There has been a growing recognition of the need for diversity and inclusion in scientific fields. This trend is reflected in the Journal of Chemical Information and Modeling (JCIM), where there has been a gradual increase in the number of papers that embrace this diversity. In this viewpoint, we analyze the evolution of the profile of papers published in JCIM from 1996 to 2022 addressing three diversity criteria, namely interdisciplinarity, geographic and gender distributions, and their impact on citation patterns.

Effect of Flattened Structures of Molecules and Materials on Machine Learning Model Training.

A key aspect of producing accurate and reliable machine learning models for the prediction of properties of quantum chemistry (QC) data is identifying possible data characteristics that may negatively influence model training. In previous work, we identified that molecules and materials with a low volume of the convex hull (VCH) of atomic positions may be harmful in model training and a source of prediction outliers. In this paper, we extend this analysis further and develop a biased sampling study to evaluate the influence of VCH on the training data of a model using different structures of molecules and materials.

Systematic Investigation of Error Distribution in Machine Learning Algorithms Applied to the Quantum-Chemistry QM9 Data Set Using the Bias and Variance Decomposition.

Most machine learning applications in quantum-chemistry (QC) data sets rely on a single statistical error parameter such as the mean square error (MSE) to evaluate their performance. However, this approach has limitations or can even yield incorrect interpretations. Here, we report a systematic investigation of the two components of the MSE, i.

Correlation-Based Framework for Extraction of Insights from Quantum Chemistry Databases: Applications for Nanoclusters.

The amount of quantum chemistry (QC) data is increasing year by year due to the continuous increase of computational power and development of new algorithms. However, in most cases, our atom-level knowledge of molecular systems has been obtained by manual data analyses based on selected descriptors. In this work, we introduce a data mining framework to accelerate the extraction of insights from QC datasets, which starts with a featurization process that converts atomic features into molecular properties (AtoMF).