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Schistosomiasis, caused by helminth flatworms of the genus Schistosoma, is an infectious disease mainly associated with poverty that affects millions of people worldwide. Since treatment for this disease relies only on the use of praziquantel, there is an urgent need to identify new antischistosomal drugs. Piplartine is an amide alkaloid found in several Piper species (Piperaceae) that exhibits antischistosomal properties. The aim of this study was to evaluate the structure–function relationship between piplartine and its five synthetic analogues (19A, 1G, 1M, 14B and 6B) against Schistosoma mansoni adult worms, as well as its cytotoxicity to mammalian cells using murine fibroblast (NIH-3T3) and BALB/cN macrophage (J774A.1) cell lines. In addition, density functional theory calculations and in silico analysis were used to predict physicochemical and toxicity parameters. Bioassays revealed that piplartine is active against S. mansoni at low concentrations (5–10 µM), but its analogues did not. In contrast, based on 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and flow cytometry assays, piplartine exhibited toxicity in mammalian cells at 785 µM, while its analogues 19A and 6B did not reduce cell viability at the same concentrations. This study demonstrated that piplartine analogues showed less activity against S. mansoni but presented lower toxicity than piplartine. [ABSTRACT FROM AUTHOR]

Traditional automated theorem provers have relied on manually tuned heuristics to guide how they perform proof search. Recently, however, there has been a surge of interest in the design of learning mechanisms that can be integrated into theorem provers to improve their performance automatically. In this work, we describe TRAIL (Trial Reasoner for AI that Learns), a deep learning-based approach to theorem proving that characterizes core elements of saturation-based theorem proving within a neural framework. TRAIL leverages (a) an effective graph neural network for representing logical formulas, (b) a novel neural representation of the state of a saturation-based theorem prover in terms of processed clauses and available actions, and (c) a novel representation of the inference selection process as an attention-based action policy. We show through a systematic analysis that these components allow TRAIL to significantly outperform previous reinforcement learning-based theorem provers on two standard benchmark datasets (up to 36% more theorems proved). In addition, to the best of our knowledge, TRAIL is the first reinforcement learning-based approach to exceed the performance of a state-of-the-art traditional theorem prover on a standard theorem proving benchmark (solving up to 17% more theorems).

Background: Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs., Methods: In this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori., Results: We evaluated the performance of our model with ten different state-of-the-art fingerprint models and found that neural fingerprints from the deep learning model outperformed all other methods in predicting ADRs. Via feature analysis on drug structures, we identified important molecular substructures that are associated with specific ADRs and assessed their associations via statistical analysis., Conclusions: The deep learning model with feature analysis, substructure identification, and statistical assessment provides a promising solution for identifying risky components within molecular structures and can potentially help to improve drug safety evaluation.