{'fraction_non_alphanumeric': 0.02155688622754491, 'fraction_numerical': 0.008982035928143712, 'mean_word_length': 5.377862595419847, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A naive bayes classifier for prediction of multidrug resistance reversal activity on the basis of atom typing.

Multidrug resistance (MDR), the ability of cancer cells to become simultaneously resistant to different drugs, remains an unsolved challenge in cancer chemotherapy. The use of MDR reversal (MDRR) agents is a promising approach to overcome this problem. For the design and development of such agents, it would be desirable to have a reliable model to estimate the MDRR activity of compounds. Presented here is a naive Bayes classifier to categorize MDRR agents into active and inactive classes, which uses a universal, generic molecular-descriptor system.(1) The naive Bayes classifier was built from a 424 compound training set, selected from 609 druglike compounds in the publicly available "Klopman set". The model correctly predicted MDRR activities for 82.2% of 185 compounds in a testing set. The cumulative probabilities were proven useful for prioritizing the compounds for testing. The impact of attribute dependences on the performance of the classifier was examined. As an unsupervised learner with no tuning parameters, a naive Bayes classifier is capable of providing an objective comparison of the effectiveness of different molecular descriptors. The relative performance of the classifiers constructed from either an atom-type-based molecular descriptor or the long-range functional-class fingerprint descriptors FCFP_6 or FCFP_2 was compared. Employing an atom typing descriptor with the naive Bayes classification, it enables the interpretability of the resulting model, which offers extra information for the rational design of MDRR agents.