BitterDB (http://bitterdb.agri.huji.ac.il) was introduced in 2012 as a central resource for information on bitter-tasting molecules and their receptors. The information in BitterDB is frequently used for choosing suitable ligands for experimental studies, for developing bitterness predictors, for analysis of receptors promiscuity and more. Here, we describe a major upgrade of the database, including significant increase in content as well as new features. BitterDB now holds over 1000 bitter molecules, up from the initial 550. When available, quantitative sensory data on bitterness intensity as well as toxicity information were added. For 270 molecules, at least one associated bitter taste receptor (T2R) is reported. The overall number of ligand–T2R associations is now close to 800. BitterDB was extended to several species: in addition to human, it now holds information on mouse, cat and chicken T2Rs, and the compounds that activate them. BitterDB now provides a unique platform for structure-based studies with high-quality homology models, known ligands, and for the human receptors also data from mutagenesis experiments, information on frequently occurring single nucleotide polymorphisms and links to expression levels in different tissues.

Bitter taste is an innately aversive taste modality that is considered to protect animals from consuming toxic compounds. Yet, bitterness is not always noxious and some bitter compounds have beneficial effects on health. Hundreds of bitter compounds were reported (and are accessible via the BitterDB http://bitterdb.agri.huji.ac.il/dbbitter.php), but numerous additional bitter molecules are still unknown. The dramatic chemical diversity of bitterants makes bitterness prediction a difficult task. Here we present a machine learning classifier, BitterPredict, which predicts whether a compound is bitter or not, based on its chemical structure. BitterDB was used as the positive set, and non-bitter molecules were gathered from literature to create the negative set. Adaptive Boosting (AdaBoost), based on decision trees machine-learning algorithm was applied to molecules that were represented using physicochemical and ADME/Tox descriptors. BitterPredict correctly classifies over 80% of the compounds in the hold-out test set, and 70–90% of the compounds in three independent external sets and in sensory test validation, providing a quick and reliable tool for classifying large sets of compounds into bitter and non-bitter groups. BitterPredict suggests that about 40% of random molecules, and a large portion (66%) of clinical and experimental drugs, and of natural products (77%) are bitter.

The role of bitter taste-one of the few basic taste modalities-is commonly assumed to signal toxicity and alert animals against consuming harmful compounds. However, it is known that some toxic compounds are not bitter and that many bitter compounds have negligible toxicity while having important health benefits. Here we apply a quantitative analysis of the chemical space to shed light on the bitterness-toxicity relationship. Using the BitterDB dataset of bitter molecules, The BitterPredict prediction tool, and datasets of toxic compounds, we quantify the identity and similarity between bitter and toxic compounds. About 60% of the bitter compounds have documented toxicity and only 56% of the toxic compounds are known or predicted to be bitter. The LD value distributions suggest that most of the bitter compounds are not very toxic, but there is a somewhat higher chance of toxicity for known bitter compounds compared to known nonbitter ones. Flavonoids and alpha acids are more common in the bitter dataset compared with the toxic dataset. In contrast, alkaloids are more common in the toxic datasets compared to the bitter dataset. Interestingly, no trend linking LD values with the number of activated bitter taste receptors (TAS2Rs) subtypes is apparent in the currently available data. This is in accord with the newly discovered expression of TAS2Rs in several extra-oral tissues, in which they might be activated by yet unknown endogenous ligands and play non-gustatory physiological roles. These results suggest that bitter taste is not a very reliable marker for toxicity, and is likely to have other physiological roles. © 2017 IUBMB Life, 69(12):938-946, 2017.

Machine Learning (ML) has become an essential asset for the life sciences and medicine. We selected 300 articles describing ML applications from 17 journals sampling 26 different fields between 2011 and 2018. Independent evaluation by two readers highlighted three results.First, only half of the articles shared software, 64% shared data, and 81% applied any kind of evaluation. Although these aspects are crucial to ensure validity and reliability of ML applications, they were met more by publications in lower-ranked journals. Second, the authors' scientific background highly influenced how technical aspects were addressed: reproducibility and computational evaluation methods were more prominent with computational co-authors; experimental proofs more with experimentalists. Third, 73% of the ML applications resulted from interdisciplinary collaborations comprising authors from at least two of the three disciplines: computational sciences, experimental biology, medicine._deleted_ The data suggested collaborations between computational and experimental scientists to generate more computationally sound and impactful work integrating knowledge.Furthermore, such collaborations provide opportunities to both sides: computational scientists are given access to novel and challenging real-world biological data increasing the scientific impact of their research, and experimentalists benefit from more in-depth computational analyses improving the technical correctness of work.

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