Artificial Intelligence–Driven Computational Approaches in the Development of Anticancer Drugs
The integration of AI has revolutionized cancer drug development, transforming the landscape of drug discovery through sophisticated computational techniques. AI-powered models and algorithms have enhanced computer-aided drug design (CADD), offering unprecedented precision in identifying potential anticancer compounds. Traditionally, cancer drug design has been a complex, resource-intensive process, but AI introduces new opportunities to accelerate discovery, reduce costs, and optimize efficiency. This manuscript delves into the transformative applications of AI-driven methodologies in predicting and developing anticancer drugs, critically evaluating their potential to reshape the future of cancer therapeutics while addressing their challenges and limitations.
Authors
- Ravi Salgia (ORCID: https://orcid.org/0000-0001-9643-7626)
- G. D. Singhal (ORCID: https://orcid.org/0000-0003-0859-2285)
- Pankaj Garg (ORCID: https://orcid.org/0000-0002-0800-3578)
- David Horne (ORCID: https://orcid.org/0000-0002-9300-1082)
- Sharad S. Singhal (ORCID: https://orcid.org/0000-0002-6415-8160)
- Prakash Kulkarni (ORCID: https://orcid.org/0000-0001-6736-4333)
Institutions
- City Of Hope National Medical Center (US)
- Sarojini Naidu Medical College (IN)
- City of Hope (US)
- GLA University (IN)
Publication Details
- Journal
- Cancers
- Published
- 2024-11-20
- DOI
- https://doi.org/10.3390/cancers16223884
- Citations
- 34
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 12.29