Drug-Target Interactions Prediction at Scale: The Komet Algorithm with the LCIdb Dataset.

Drug-target interactions (DTIs) prediction algorithms are used at various stages of the drug discovery process. In this context, specific problems such as deorphanization of a new therapeutic target or target identification of a drug candidate arising from phenotypic screens require large-scale predictions across the protein and molecule spaces. DTI prediction heavily relies on supervised learning algorithms that use known DTIs to learn associations between molecule and protein features, allowing for the prediction of new interactions based on learned patterns.

Nutritional contributions and processability of pasta made from climate-smart, sustainable crops: A critical review.

Total or partial replacement of traditional durum wheat semolina (DWS) by alternative flours, such as legumes or wholegrain cereals in pasta improves their nutritional quality and can make them interesting vector for fortification. Climate-smart gluten-free (C-GF) flours, such as legumes (bambara groundnut, chickpea, cowpea, faba bean, and pigeon pea), some cereals (amaranth, teff, millet, and sorghum), and tubers (cassava and orange fleshed sweet potato), are of high interest to face ecological transition and develop sustainable food systems. In this review, an overview and a critical analysis of their nutritional potential for pasta production and processing conditions are undertaken.

Exploring isofunctional molecules: Design of a benchmark and evaluation of prediction performance.

Identification of novel chemotypes with biological activity similar to a known active molecule is an important challenge in drug discovery called 'scaffold hopping'. Small-, medium-, and large-step scaffold hopping efforts may lead to increasing degrees of chemical structure novelty with respect to the parent compound. In the present paper, we focus on the problem of large-step scaffold hopping.

Molecular International Prognostic Scoring System for Myelodysplastic Syndromes.

BACKGROUND: Risk stratification and therapeutic decision-making for myelodysplastic syndromes (MDS) are based on the International Prognostic Scoring System–Revised (IPSS-R), which considers hematologic parameters and cytogenetic abnormalities. Somatic gene mutations are not yet used in the risk stratification of patients with MDS. METHODS: To develop a clinical-molecular prognostic model (IPSS-Molecular [IPSS-M]), pretreatment diagnostic or peridiagnostic samples from 2957 patients with MDS were profiled for mutations in 152 genes.

Subject-specific segregation of functional territories based on deep phenotyping.

Functional magnetic resonance imaging (fMRI) has opened the possibility to investigate how brain activity is modulated by behavior. Most studies so far are bound to one single task, in which functional responses to a handful of contrasts are analyzed and reported as a group average brain map. Contrariwise, recent data-collection efforts have started to target a systematic spatial representation of multiple mental functions.