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In this report, we introduce a set of aggregation operators (AOs) to calculate global and local (group and atom type) molecular descriptors (MDs) as a generalization of the classical approach of molecular encoding using the sum of the atomic (or fragment) contributions. These AOs are implemented in a new and free software denominated MD-LOVIs ( http://tomocomd.com/md-lovis ), which allows for the calculation of MDs from atomic weights vector and LOVIs (local vertex invariants). This software was developed in Java programming language and employed the Chemical Development Kit (CDK) library for handling chemical structures and the calculation of atomic weights. An analysis of the complexities of the algorithms presented herein demonstrates that these aspects were efficiently implemented. The calculation speed experiments show that the MD-LOVIs software has satisfactory behavior when compared to software such as Padel, CDKDescriptor, DRAGON and Bluecal software. Shannon's entropy (SE)-based variability studies demonstrate that MD-LOVIs yields indices with greater information content when compared to those of popular academic and commercial software. A principal component analysis reveals that our approach captures chemical information orthogonal to that codified by the DRAGON, Padel and Mold2 software, as a result of the several generalizations in MD-LOVIs not used in other programs. Lastly, three QSARs were built using multiple linear regression with genetic algorithms, and the statistical parameters of these models demonstrate that the MD-LOVIs indices obtained with AOs yield better performance than those obtained when the summation operator is used exclusively. Moreover, it is also revealed that the MD-LOVIs indices yield models with comparable to superior performance when compared to other QSAR methodologies reported in the literature, despite their simplicity. The studies performed herein collectively demonstrated that MD-LOVIs software generates indices as simple as possible, but not simpler and that use of AOs enhances the diversity of the chemical information codified, which consequently improves the performance of traditional MDs.

This paper brings together two accounts of induction that appear to be in opposition: John Norton's material account of induction (2003, 2010, manuscript) and Schurz' account of the universal optimality of meta-induction (2008, 2017, 2019). According to the material account of induction, all reliable rules of 'induction' are local and context-dependent. Here "induction" is understood in the sense of object-induction, i.e., induction applied at the object-level of events. In contrast, Schurz' account proceeds from the demonstration that there are universally optimal rules of meta-induction, i.e., rules of induction applied at the level of competing methods of prediction, including methods of object-induction. The two accounts are not in opposition; on the contrary, they agree on most questions related to the problem of induction. Beyond this agreement the two accounts are complementary: the material account suffers from a justificational circularity or regress problem that the meta-induction account can solve. On the other hand, the meta-inductive account abstracts from domain-specific aspects of object-induction that are supplied by the material account., (Copyright © 2019 Elsevier Ltd. All rights reserved.)