Sort & Slice: a simple and superior alternative to hash-based folding for extended-connectivity fingerprints.

Extended-connectivity fingerprints (ECFPs) are a ubiquitous tool in current cheminformatics and molecular machine learning, and one of the most prevalent molecular feature extraction techniques used for chemical prediction. Atom features learned by graph neural networks can be aggregated to compound-level representations using a large spectrum of graph pooling methods. In contrast, sets of detected ECFP substructures are by default transformed into bit vectors using only a simple hash-based folding procedure.

Mind your prevalence!

Multiple metrics are used when assessing and validating the performance of quantitative structure-activity relationship (QSAR) models. In the case of binary classification, balanced accuracy is a metric to assess the global performance of such models. In contrast to accuracy, balanced accuracy does not depend on the respective prevalence of the two categories in the test set that is used to validate a QSAR classifier.

Exploring QSAR models for activity-cliff prediction.

Introduction And Methodology: Pairs of similar compounds that only differ by a small structural modification but exhibit a large difference in their binding affinity for a given target are known as activity cliffs (ACs). It has been hypothesised that QSAR models struggle to predict ACs and that ACs thus form a major source of prediction error. However, the AC-prediction power of modern QSAR methods and its quantitative relationship to general QSAR-prediction performance is still underexplored.

Federated learning for molecular discovery.

Federated Learning enables machine learning across multiple sources of data and alleviates the risk of leaking private information between partners thereby encouraging knowledge sharing and collaborative modelling. Hence, Federated Learning opens the ways to a new generation of improved models. Domains involving molecular informatics, like Drug Discovery, are progressively adopting Federated Learning; this review describes the main projects and applications of Federated Learning for molecular discovery with a special focus on their benefits and the remaining challenges.

3D vertical alveolar crest augmentation in the posterior mandible using the tunnel technique: A 10-year clinical study.

Purpose: To evaluate the short- and long-term outcomes of vertical 3D bone augmentation in the posterior mandible, performed using the split bone block technique with a tunnel technique.

Materials And Methods: Patients were treated for vertical and horizontal alveolar bone defects without simultaneous implant placement and followed up for at least 10 years postoperatively. Autogenous bone blocks were harvested from the mandibular retromolar area following the MicroSaw protocol (Dentsply Sirona, Charlotte, NC, USA).