Beyond the Arbitrariness of Drug-Likeness Rules: Rough Set Theory and Decision Rules in the Service of Drug Design
Lipinski’s Rule of Five and Ghose filter are empirical guidelines for evaluating the drug-likeness of a compound, suggesting that orally active drugs typically fall within specific ranges for molecular descriptors such as hydrogen bond donors and acceptors, weight, and lipophilicity. We revisit thes...
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MDPI AG
2024-10-01
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| Online Access: | https://www.mdpi.com/2076-3417/14/21/9966 |
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| author | Grzegorz Miebs Adam Mielniczuk Miłosz Kadziński Rafał A. Bachorz |
| author_facet | Grzegorz Miebs Adam Mielniczuk Miłosz Kadziński Rafał A. Bachorz |
| author_sort | Grzegorz Miebs |
| collection | DOAJ |
| description | Lipinski’s Rule of Five and Ghose filter are empirical guidelines for evaluating the drug-likeness of a compound, suggesting that orally active drugs typically fall within specific ranges for molecular descriptors such as hydrogen bond donors and acceptors, weight, and lipophilicity. We revisit these practices and offer a more analytical perspective using the Dominance-based Rough Set Approach (DRSA). By analyzing representative samples of drug and non-drug molecules and focusing on the same molecular descriptors, we derived decision rules capable of distinguishing between these two classes systematically and reproducibly. This way, we reduced human bias and enabled efficient knowledge extraction from available data. The performance of the DRSA model was rigorously validated against traditional rules and available machine learning (ML) approaches, showing a significant improvement over empirical rules while achieving comparable predictive accuracy to more complex ML methods. Our rules remain simple and interpretable while being characterized by high sensitivity and specificity. |
| format | Article |
| id | doaj-art-2c42f7067e624391a8895b3b00e010f6 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-2c42f7067e624391a8895b3b00e010f62024-11-08T14:33:58ZengMDPI AGApplied Sciences2076-34172024-10-011421996610.3390/app14219966Beyond the Arbitrariness of Drug-Likeness Rules: Rough Set Theory and Decision Rules in the Service of Drug DesignGrzegorz Miebs0Adam Mielniczuk1Miłosz Kadziński2Rafał A. Bachorz3Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznań, PolandInstitute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznań, PolandInstitute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznań, PolandInstitute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznań, PolandLipinski’s Rule of Five and Ghose filter are empirical guidelines for evaluating the drug-likeness of a compound, suggesting that orally active drugs typically fall within specific ranges for molecular descriptors such as hydrogen bond donors and acceptors, weight, and lipophilicity. We revisit these practices and offer a more analytical perspective using the Dominance-based Rough Set Approach (DRSA). By analyzing representative samples of drug and non-drug molecules and focusing on the same molecular descriptors, we derived decision rules capable of distinguishing between these two classes systematically and reproducibly. This way, we reduced human bias and enabled efficient knowledge extraction from available data. The performance of the DRSA model was rigorously validated against traditional rules and available machine learning (ML) approaches, showing a significant improvement over empirical rules while achieving comparable predictive accuracy to more complex ML methods. Our rules remain simple and interpretable while being characterized by high sensitivity and specificity.https://www.mdpi.com/2076-3417/14/21/9966drug-likenessmultiple criteria decision analysisdrug designbiological activitydominance-based rough set approachdecision rules |
| spellingShingle | Grzegorz Miebs Adam Mielniczuk Miłosz Kadziński Rafał A. Bachorz Beyond the Arbitrariness of Drug-Likeness Rules: Rough Set Theory and Decision Rules in the Service of Drug Design Applied Sciences drug-likeness multiple criteria decision analysis drug design biological activity dominance-based rough set approach decision rules |
| title | Beyond the Arbitrariness of Drug-Likeness Rules: Rough Set Theory and Decision Rules in the Service of Drug Design |
| title_full | Beyond the Arbitrariness of Drug-Likeness Rules: Rough Set Theory and Decision Rules in the Service of Drug Design |
| title_fullStr | Beyond the Arbitrariness of Drug-Likeness Rules: Rough Set Theory and Decision Rules in the Service of Drug Design |
| title_full_unstemmed | Beyond the Arbitrariness of Drug-Likeness Rules: Rough Set Theory and Decision Rules in the Service of Drug Design |
| title_short | Beyond the Arbitrariness of Drug-Likeness Rules: Rough Set Theory and Decision Rules in the Service of Drug Design |
| title_sort | beyond the arbitrariness of drug likeness rules rough set theory and decision rules in the service of drug design |
| topic | drug-likeness multiple criteria decision analysis drug design biological activity dominance-based rough set approach decision rules |
| url | https://www.mdpi.com/2076-3417/14/21/9966 |
| work_keys_str_mv | AT grzegorzmiebs beyondthearbitrarinessofdruglikenessrulesroughsettheoryanddecisionrulesintheserviceofdrugdesign AT adammielniczuk beyondthearbitrarinessofdruglikenessrulesroughsettheoryanddecisionrulesintheserviceofdrugdesign AT miłoszkadzinski beyondthearbitrarinessofdruglikenessrulesroughsettheoryanddecisionrulesintheserviceofdrugdesign AT rafałabachorz beyondthearbitrarinessofdruglikenessrulesroughsettheoryanddecisionrulesintheserviceofdrugdesign |