https://orcid.org/0000-0002-0557-5714
References
- Zhou J , JiangX , HeSet al. Rational design of multitarget-directed ligands: strategies and emerging paradigms. J. Med. Chem.62(20), 8881–8914 (2019).
- Proschak E , StarkH , MerkD. Polypharmacology by design: a medicinal chemist's perspective on multitargeting compounds. J. Med. Chem.62(2), 420–444 (2019).
- Anighoro A , BajorathJ , RastelliG. Polypharmacology: challenges and opportunities in drug discovery. J. Med. Chem.57(19), 7874–7887 (2014).
- Cohen P , CrossD , JännePA. Kinase drug discovery 20 years after imatinib: progress and future directions. Nature Rev. Drug Discov.20(7), 551–569 (2021).
- Sun D , ZhaoY , ZhangSet al. Dual-target kinase drug design: current strategies and future directions in cancer therapy. Eur. J. Med. Chem.188(1), e112025 (2020).
- Zhang P , XuS , ZhuZet al. Multi-target design strategies for the improved treatment of Alzheimer's disease. Eur. J. Med. Chem.176(1), 228–247 (2019).
- Benek O , KorabecnyJ , SoukupO. A perspective on multi-target drugs for Alzheimer's disease. Trends Pharmacol. Sci.41(7), 434–445 (2020).
- Chaudhari R , TanZ , HuangBet al. Computational polypharmacology: a new paradigm for drug discovery. Expert Opin. Drug Discov.12(3), 279–291 (2017).
- Pushpakom S , IorioF , EyersPAet al. Drug repurposing: progress, challenges and recommendations. Nature Rev. Drug Discov.18(1), 41–58 (2019).
- Talevi A , BelleraCL. Challenges and opportunities with drug repurposing: finding strategies to find alternative uses of therapeutics. Expert Opin. Drug Discov.15(4), 397–401 (2020).
- Gilberg E , BajorathJ. Recent progress in structure-based evaluation of compound promiscuity. ACS Omega4(2), 2758–2765 (2019).
- Bajorath J . Structural characteristics of compounds with multitarget activity. Future Drug Discov.3(2), FDD60 (2021).
- Feldmann C , BajorathJ. Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations. Sci. Rep.11(1), e7863 (2021).
- Belle V , PapantonisI. Principles and practice of explainable machine learning. Front. Big Data4(1), e688969 (2021).
- Gunning D , StefikM , ChoiJet al. XAI – explainable artificial intelligence. Sci. Rob.4(37), eaay7120 (2019).
- Lundberg SM , LeeS. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst.30(1), 4766–4775 (2017).
- Lundberg SM , ErionG , ChenHet al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell.2(1), 56–67 (2020).
- Rodríguez-Pérez R , BajorathJ. Interpretation of compound activity predictions from complex machine learning models using local approximations and Shapley values. J. Med. Chem.63(16), 8761–8777 (2020).
- Feldmann C , PhilippsM , BajorathJ. Explainable machine learning predictions of dual-target compounds reveal characteristic structural features. Sci. Rep.11(1), e21594 (2021).
- Feldmann C , BajorathJ. Differentiating inhibitors of closely related protein kinases with single- or multi-target activity via explainable machine learning and feature analysis. Biomolecules12(4), e557 (2022).