dataesr/scientific-paragraphs-categorization huggingface dataset:
BitterPredict BitterPredict is a bitter prediction tool published in 2017  [53]. This work is developed in the commercial MATLAB environment and the code is available on GitHub (https:// github. com/ Niv-Lab/ Bitte rPred ict1). The users should provide an Excel or CSV file with calculated properties by the commercial Schrödinger software and QikProp package. Among random molecules screened by the tool, a high percentage is represented by bitter compounds. These include many synthetic molecules (66% of drugs are bitter) and natural compounds (up to 77%). It is worth mentioning the relatively high percentage of bitter food (38%), considering the natural aversion of humans towards the bitter taste. Despite its great functionality, high accuracy (~ 80%), and the ability to screen a general chemical space, this tool presents some limitations, including (i) the prediction of only molecules in defined chemical space, named Bitter Domain, (ii) the inability to discriminate between weak and strong bitter compounds, and (iii) an unbalanced dataset (positive set three times smaller than the negative set).
Data preparation and model construction The dataset was processed using Maestro, Epik, and LigPrep (Schrödinger), removing uninterested structures and assigning the correct protonation state according to pH 7. 0 ± 0. 5. Then, non-neutralized molecules and molecules with identical descriptors were removed from the dataset to allow the calculation of the QikProp descriptors and obtain a non-redundant dataset. The whole prediction was made within a restricted chemical space called Bitter Domain to identify a region in which 97% of the bitter molecules is included. This domain is defined by a hydrophobicity (AlogP) range of -3 ≤ AlogP ≤ 7 and a molecular weight MW ≤ 700. This preparation procedure was applied to each database considered to build the final dataset. Bitter Set (positive set) includes all molecules considered as bitter (691): 632 structures from BitterDB and 59 molecules from literature  [71].
After data curation, 264 entries were selected as the Bitter Domain. Finally, a data set for prospective prediction was collected merging Drug-Bank approved drugs, FooDB, Natural Products Dataset from ZINC15 and ChEBI. Compounds in the Bitter Domain from these widely used DBs were 1375, 13,588, 27,474, and 27,015, respectively. Molecular descriptors (59) were calculated with Canvas (Schrödinger) and QikProp (Schrödinger). The final input dataset was divided into 30% (test set) and 70% (training set) randomly, following the hold-out method and preserving the original proportions. To avoid overfitting, models were optimized by evaluating their performance only in the hold-out test. The algorithm implemented by BitterPredict is AdaBoost. The ensemble method models used are Fitensemble and TreeBagger, which combines the outcomes of several decision trees, decreasing the impacts of overfitting and enhancing the generalization ability of the model.
Model performance The two parameters applied to evaluate the classification models were sensitivity (SE) and specificity (SP). For the training set, the SE was 91% and SP 94%, for the test set SE was 77% and SP 86%. Furthermore, a model evaluation based only on the non-bitter datasets was made among sweet, tasteless, and non-bitter flavors. In this context, the dataset that shows a better specificity was the non-bitter flavors, with a value of 86%. The BitterPredict study also analyzed the impact of diverse descriptors estimating their contribution in reducing the error. Notably, the most important descriptor was the total charge and most of the bitter molecules were positively charged presenting an ammonium ion at physiological pH. Moreover, QikProp descriptors linked to the compound toxicity seem to have a greater impact on the model if compared to general properties descriptors.
The validation of BitterPredict was performed in three phases, as follows. The three validation protocols indicated that BitterPredict allows obtaining reliable and satisfactory performance both for the bitter and non-bitter prediction. Finally, the BitterPredict classifier was applied to Drug-Bank approved, FooDB, and Natural Products Dataset from ZINC15 and ChEBI datasets (see also see Data preparation and model construction). The results highlight that the percentages of bitter molecules are, respectively, 65. 94%, 38. 36%, 77. 21%, and 43. 71%.
Data preparation and model construction The dataset contains experimentally confirmed 707 bitter compounds, derived mostly from BitterDB  [25]  and literature research  [27, 29] , and 592 non-bitter compounds (132 tasteless, 17 non-bitter, and 443 sweet). Sweet compounds were obtained from SweetnersDB, SuperSweet and previous literature  [71, 72]. The same compounds but with a different taste or from different datasets, or compounds such as salts or ions, were excluded, while all structures containing common elements were retained. e-Bitter uses Extended-Connectivity Fingerprints (ECFPs) as molecular descriptors  [61]. Similarly to e-Sweet  [49] , the implemented algorithms were KNN, SVM, GBM, RF, and DNN. Models were tested both with and without feature selection  [73]. The splitting of the dataset follows the same criterion as previously described in e-Sweet: 1030 compounds (556 bitter and 474 non-bitter), i.
Model performance The metrics employed to assess the model performance include precision, Matthews correlation coefficient (MCC), sensitivity, accuracy, specificity, F1-score, and ΔF1-score (difference between the F1-score in cross-validation and the test set). Moreover, the reliability of the developed models was accessed using the Y-randomization test, as for the e-Sweet model. Finally, an applicability domain based on the Tanimoto similarity was imple-mented to avoid non-reliable predictions on compounds highly diverse from compounds in the dataset. Starting from an initial set of 1312 models, 96 averaged models (over adopted data partitioning schemes) and 9 consensus models were tested. Best performance was obtained by top average models trained with DNN3 (ACC = 92. 0%, SP = 80. 8%, SE = 98%, MCC = 82.
iBitter-SCM The iBitter-SCM tool is the first computational model that provides a prediction of the peptides' bitter taste starting with their AA sequence independently from structural and functional information  [26]. iBitter-SCM is freely available as a web server (http:// camt. pytho nanyw here. com/ iBitt er-SCM), and all codes and datasets are also on GitHub (https:// github. com/ Shoom buato ng2527/ Bench mark-datas ets).
Data preparation and model construction The dataset BTP640 (see also Taste and Food-Related Databases chapter for further information) includes 640 molecules equally divided between bitter and non-bitter (training set 80% and test set 20%). Features representation was realized using the dipeptide composition (DPC). iBitter-SCM is based on the scoring card method (SCM), which enabled robust protein and peptide function prediction and analysis without any information regarding their structure and relying instead on the socalled propensity scores of individual peptides and amino acids. More in detail, after preparing a training dataset and an independent dataset, the workflow started by determining the initial propensity scores (init-DPS) of dipeptides using statistics and subsequently applying Genetic Algorithms (GAs) to refine and optimize the score to the so-called optimized dipeptide propensity score (opti-DPS).
Finally, the individual amino acid propensity score was again extracted by statistical methods enabling the final discrimination between bitter and non-bitter peptides employing a weighted sum with opti-DPS. In a nutshell, these scores represent the link between peptide composition and function, by directly quantifying the contribution of individual amino acids on the physical-chemical characteristics. Furthermore, informative physicochemical properties (PCPs) of individual amino acids, i. e. , their direct involvement in fundamental biological reactions and pathways, were taken from the amino acid index database (AAindex)  [74].
Data preparation and model construction The dataset used to develop the BERT4Bitter model is the same used for the iBitter-SCM method, i. e. , the BTP640  [26]  (see also Taste and food-related databases chapter for further details). Using the same 80:20 splitting ratio, the BTP640 dataset was randomly divided for training and testing. The peptide sequence featurization was achieved through the natural language processing (NLP) techniques, specifically using Pep2Vec  [75]  and FastText  [76]. Each of the 20 amino acids is considered as a word and each peptide sequence was translated into a sentence (an n-dimensional word vector). In the same framework, the importance of each amino acid in the analyzed sequences was evaluated with the TFIDF method  [77]. Three different deep-learning-based models, i.
iBitter-Fuse After the development of iBitter-SCM and BERT4Bitter, the same research group implemented an improved bitter/ non-bitter peptides predictor, called iBitter-Fuse  [56]. This model overcomes some of the main drawbacks of the previous ones, including the generalization capability linked to the feature representation, overfitting, redundancy, and the overall performance. Exploiting several feature encoding schemes and customized algorithms to identify the most informative features, iBitter-Fuse outperformed both iBitter-SCM and BERT4Bitter, establishing itself, at the moment, as the best tool for the prediction of bitter peptides. Data preparation and model construction BTP640 was again used as starting dataset as done for iBitter-SCM and BERT4Bitter, and the same 80:20 partition scheme was applied to effectively compare their performance.
BitterIntense BitterIntense is a unique tool able to quantify the bitter intensity of a query molecule, discriminating between two classes, i. e. , "not very bitter" (NVB) and "very bitter" (VB)  [57]. This tool is paramount not only for food research but also for pharma and biotechnology industries: the ability to predict the level of bitterness during the drug discovery process represents a promising opportunity for reducing delays, animal use, and financial costs. In fact, the intensely bitter taste is often associated with difficulties in taking medication, especially for children and elderly people. Bit-terIntense, published at the end of 2020, was also applied to widely known databases, such as DrugBank, and specific COVID-19 drug candidate datasets, highlighting interesting considerations regarding bitter intensity and toxicity.
Data preparation and model construction The screening of bitter compounds was performed employing the rat brief-access taste aversion (BATA) model, obtaining 34 compounds. The dataset collects BitterDB and Analyti-Con's repository of natural compounds and counts about 180 molecules with a specified bitter intensity. The bitter recognition threshold is 0. 1 mM: below this concentration, the molecules were considered "very bitter" (VB), whereas above this value "not very bitter" (NVB). Molecules without quantitative information were assigned to VB/NVB classes according to the taste descriptions. A non-bitter database of 152 randomly selected compounds from the negative set of BitterPredict was added to NVB class. Moreover, external datasets have been screened using the optimized model. Toxicity data include the FocTox dataset, i.
e. , extremely hazardous compounds and FAO/WHO food contaminants, and the CombiTox dataset, i. e. , a combination of DSSTox-the Distributed Structure-Searchable Toxicity Database and Toxin and Toxin-Target Database version 2. 0 (T3DB)  [40]. Hepatotoxicity data were retrieved from FDA's DILIrank dataset  [79]. Finally, external datasets include Drug-Bank  [36] , consisting of approved and experimental drugs, COVID-19 drugs, and their targets retrieved from "Coronavirus Information. IUPHAR/BPS Guide to Pharmacology" and Natural products atlas (NPatlas, version 2019_08)  [38]. SMILES were processed using Maestro (Schrödinger) (3D reconstruction, protonation at pH 7. 0 ± 0. 5, removal of additional molecules, and generation of conformers). Molecular descriptors were calculated using Canvas (Schrödinger) and were divided into three groups, i.
e. , Physicochemical, Ligfilter, and QikProp. Compounds not having one of these were excluded. Feature selection was performed using the feature importance gain score, obtaining a total of 55 features (from the starting 235). The dataset was randomly divided into a training set (169 VB and 324 NVB), a test dataset (43 VB and 80 NVB), and the hold-out set for an external evaluation  (31 VB and 74 NVB). The algorithm used was the Extreme Gradient Boosting (XGBoost). Logarithmic loss and binary classification error rate were selected to monitor step by step the algorithm performance and stop it when the improvement subsides. Parameters of the models were tuned through a tenfold CV.
Model performance Performance evaluation was made for the three different datasets, i. e. , training set (with tenfold 1 3 CV), test set, and hold-out set, and for each of them, accuracy (ACC), precision, sensitivity (SENS), and F1-score were calculated (ACC over 80% in all sets, PRC: 80%, 71%, 63%; SE: 85%, 86%, 77%; F1-score: 82 ± 5%, 78%, and 70%, respectively). From these results, there are more false positives than false negatives, indicating the maximization of the identification of very bitter compounds. From the analysis of the feature importance, the algorithm pointed out the role of the molecule's size and molar refractivity (a measure of polarizability) in determining the bitterness level, suggesting also a correlation between molecule size and bitter intensity. BitterIntense applied to toxic databases, i.
e. , FocTox and CombiTox datasets, revealed that only a small portion (about 10%) of toxic substances are intensively bitter. The use of the BitterIntense model to the DILIrank dataset allows the evaluation of the correlation between bitterness level and hepatotoxicity, showing that most of the drugs (729) were classified as NVB. Then, approved and experimental compounds from Drugbank database (10,170 compounds) and natural compounds from NPatlas (24,805 compounds) were screened, showing that almost half of microbial natural products, but only 23. 7% of drug candidates are predicted as VB. Finally, 34 potential drug candidates against COVID-19 retrieved from "Coronavirus Information-IUPHAR/BPS Guide to Pharmacology" were classified, showing that 41. 2% of them are likely VB, thus significantly higher than the percentage of VB drug compounds from Drugbank, suggesting a possible involvement of the bitter taste and bitter receptors in this disease.
Umami prediction iUmami-SCM iUmami-SCM is the first umami taste predictor based on umami peptide primary sequence information  [28]. iUmami-SCM is a webserver (http:// camt. pytho nanyw here. com/ iUmami-SCM) and related datasets are available on GitHub (https:// github. com/ Shoom buato ng/ Datas et-Code/ tree/ master/ iUmami).
Data preparation and model construction The dataset, known as UMP442, contains 140 proved umami peptides and 304 bitter structures taken from iBitter-SCM as negative samples (see also Taste and food-related databases for further details). Interestingly, the peptide length of both positive and negative samples is less than 10 amino acid residues. UMP442 was divided randomly into two parts keeping the unbalancing between the positive and negative data: the training set, made up of 80% of the dataset, was employed for the generation of an initial scoring card with a statistical approach and its optimization through a GA algorithm and the independent set (UMP-IND), composed of 20% of the dataset, was employed for performance evaluation. Dipeptides propensity scores and informative physicochemical properties were employed as features in this model.
Data preparation and model construction The dataset contains 517 artificial and natural sweet compounds, derived from SuperSweet, and 685 bitter molecules, taken from Bit-terDB. Instant Jchem software was employed for molecules standardization. All duplicated molecules were removed. Four different binary fingerprints were calculated with RDKit node in KNIME: Morgan fingerprint, Atom pair fingerprints, Torsion fingerprint, and Morgan Feat fingerprints. Training and test sets were obtained with an 80:20 partitioning scheme, keeping the balance between the two classes. To avoid overfitting, a leave-one-out cross-validation (LOO) was performed. A Random Forest with Tree Ensemble Learner and Predictor nodes in KNIME  [80]  was implemented, and a Bayesian-based features detection was applied to analyze the important and frequent features.
3 The performance was also calculated in an external validation set, which includes bitter, sweet, and tasteless molecules. Despite tasteless molecules are not included in the training dataset, the model provided good results, and this suggests the features employed are specific for bitter and sweet prediction. Interestingly, the screening of Super-Natural II, DrugBank approved drug molecules and Pro-Tox, including oral toxicity compounds, showed that most molecules exhibited bitter features and toxic substances are normally bitter.
BitterSweet BitterSweet is a freely accessible tool created in 2019 to classify bitter-sweet molecules  [59]. To boost the progress in the knowledge of bitter-sweet taste molecular basis, the creators of this tool make all datasets, models, and even endto-end software publicly available (https:// cosyl ab. iiitd. edu. in/ bitte rsweet/; https:// github. com/ cosyl abiiit/ bitte rsweet).
Data preparation and model construction The dataset was created avoiding two problems observed in previous studies: the use of unverified flavor molecules in the training dataset, as happened in BitterPredict and BitterX, and the use of only experimentally verified data, leading to a drastic reduction in the dataset size. The dataset contains around thousands of chemicals among bitter, non-bitter, sweet, and non-sweet compounds retrieved from literature  [27, 29, 53] , pre-existing databases, i. e. , SuperSweet, The Good Scents Company Database, BitterDB and books, i. e. , Fenaroli's Handbook of Flavor Ingredient And Biochemical Targets of Plant Bioactive Compounds. Moreover, as the control for bitter and sweet prediction, tasteless, and contrasting taste compounds, derived from ToxNet, TastesDB, and Fenaroli's Handbook of Flavor Ingredient, were introduced in the dataset.
The canonical SMILES were extracted through Open-Babel  [81]. Duplicate structures, peptides, molecules with only three atoms, and salt ions were removed, while only the lowest energy conformer for each molecule was retained. The chirality of the molecule was preserved. The 3D conformation and protonation state at physiological pH (7 ± 0. 5) were carried out using Epik  [82]  and LigPrep (Schrödinger). The training dataset for bitter/non-bitter prediction included 813 bitter molecules as positive data and 1444 sweet and tasteless molecules as the negative set, while for sweet/non-sweet prediction, it consisted of 1139 sweet molecules as the positive set and 1066 bitter and tasteless compounds as the negative set. The test dataset was formed by 105 bitter and 66 non-bitter structures in the bitter prediction and 108 sweet and 53 bitter/tasteless molecules in sweet prediction.
3%, PR-AUC of 95%, the sensitivity of 79%, the specificity of 88%, and the F1-score of 86%. However, in their online tool, they employed ChemoPy descriptors with the RF-PCA algorithm as the performance of open-source descriptors were comparable to those obtained through proprietary software. The results achieved with the BitterSweet model: ROC-AUC of 84% and 88%, PR-AUC of 93% and 93%, the sensitivity of 59% and 79%, the specificity of 94% and 85%, and the F1-score of 73% and 84%, and all the results were reported for sweet and bitter prediction, respectively. The BitterSweet model was also applied to several specialized chemical databases, i. e. , SuperSweet, FlavorDB, FooDB, DSSTox, SuperNatural II, and DrugBank, revealing that the majority of natural, toxic, and drug-like molecules are bitter, whereas for food molecules, there was the same amount of bitter and sweet molecules.
Data preparation and model construction The dataset contains 2011 sweet compounds, collected from the SuperSweet database and BitterSweetForest tool, 1612 bitter molecules derived from the BitterDB database and BitterSweetForest tool, and 1347 sour compounds obtained from ChEMBL  [35]  and manually edited from the literature sources  [84]. Furthermore, the bitter receptor data contain 356 ligands that interact with TAS2Rs receptor extracted from Bit-terDB, ChEMBL and literature. Different structures were removed from the database, such as ambiguous compounds, salt and inconclusive entries, and then standardized through RDkit in KNIME  [80]. Each dataset was split into two parts, preserving the positive/negative ratio: the training set made up of 80% of each set of molecules, i. e. , 1068 molecules for sweet, 1289 compounds for bitter and 1214 structures for sour, and the remaining chemicals were employed for the external validation set.
The inactive dataset used in each model were different: bitter and tasteless compounds were used as inactive compounds for the sweet prediction and sweet and tasteless compounds were employed as inactive compounds for the bitter prediction, while the sour prediction used a ligand-based approach due to the pH and acid influence present in foods. Moreover, a tenfold CV was applied for model optimisation, keeping the ratio of active and inactive structures constant. Each VirtualTaste model was based on an RF algorithm, following BitterSweetForest, the previous tool developed by the same research group  [58]. To deal with the negative effect of the unbalanced dataset, different data sampling methods were applied: the Synthetic Minority Over-Sampling Technique-using Tanimoto Coefficient (SMOTETC) technique for VirtualSweet, the Synthetic Minority Over-Sampling Technique-using Value Difference Metric (SMOTEVDM) method for VirtualBitter and the Augmented Random Over-Sampling (AugRandOS) method for VirtualSour  [85].
A similarity-based method was employed for the prediction of bitter receptors  [86] : the similarity between the query molecule and known bitter compounds is evaluated using the Tanimoto Coefficient and the relative target bitter receptor is then consequently predicted. VirtualSweet and VirtualBitter models were also used for taste prediction of approved drugs and natural compounds-1969 chemical compounds from DrugBank database and 326,000 from SuperNatural II. for sensitivity, and 88% for F1-score; VirtualSour 99% for ROC-AUC, 97% for the accuracy, 99% for specificity, 80% for sensitivity, and 84% for F1-score. In conclusion, Virtu-alTaste is the first tool able to predict with reliable results three different taste qualities and achieve comparable or better performance compared to similar tools.
Discussion In this section, a detailed comparison between all the abovedescribed taste prediction tools is provided. The performance of the classification and regressor models is summarized in Tables  4  and 5 , respectively. It is noteworthy that these comparative results were not obtained on the same datasets and different evaluation metrics were used in each analyzed work. Data in the tables refer to performance on the test set. At present, it is evident that there is a net prevalence of tools for predicting sweet and bitter tastes. It is worth noting that only one example to predict the umami taste (iUmami-SCM) and one for the sour taste (VirtualSour in Virtual-Taste) exist, and no tools for predicting the saltiness have been released, as far as the authors know. Moreover, the definition of a regression algorithm was possible only for the sweet taste (Chéron Sweet Regressor, Goel Sweet Regressor, and PrediSweet) since, to date, no database for other taste sensations provides quantitative data concerning the level of the perceived taste.