{'fraction_non_alphanumeric': 0.008917197452229299, 'fraction_numerical': 0.0, 'mean_word_length': 5.390243902439025, '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}}
Parallel data mining for pharmacophore discovery

Rapid and effective design of new drugs to combat new strains of antibiotic resistant organisms, more effectively treat chronic conditions, and provide other life sustaining treatment is a key challenge for the medical industry. Current drug design methodologies can take several years just in the initial chemical evaluation stages before compounds can be created for animal and human testing. This paper presents some recent research results in a new parallel machine learning approach that can expedite the drug design cycle. An inductive logic programming search has been reformulated and parallelized to run on an eight node Beowulf cluster. Initial testing with several data sets indicate almost linear speedup using the cluster.