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AIDDISON: Empowering Drug Discovery with AI/ML and CADD Tools in a Secure, Web-Based SaaS Platform.
Rusinko, Andrew; Rezaei, Mohammad; Friedrich, Lukas; Buchstaller, Hans-Peter; Kuhn, Daniel; Ghogare, Ashwini.
Affiliation
  • Rusinko A; MilliporeSigma, 400 Summit Drive, Burlington, Massachusetts 01803, United States.
  • Rezaei M; MilliporeSigma, 400 Summit Drive, Burlington, Massachusetts 01803, United States.
  • Friedrich L; Merck Healthcare KGaA, Medicinal Chemistry and Drug Design, Darmstadt 64293, Germany.
  • Buchstaller HP; Merck Healthcare KGaA, Medicinal Chemistry and Drug Design, Darmstadt 64293, Germany.
  • Kuhn D; Merck Healthcare KGaA, Medicinal Chemistry and Drug Design, Darmstadt 64293, Germany.
  • Ghogare A; MilliporeSigma, 400 Summit Drive, Burlington, Massachusetts 01803, United States.
J Chem Inf Model ; 64(1): 3-8, 2024 01 08.
Article in En | MEDLINE | ID: mdl-38134123
ABSTRACT
The widespread proliferation of artificial intelligence (AI) and machine learning (ML) methods has a profound effect on the drug discovery process. However, many scientists are reluctant to utilize these powerful tools due to the steep learning curve typically associated with them. AIDDISON offers a convenient, secure, web-based platform for drug discovery, addressing the reluctance of scientists to adopt AI and ML methods due to the steep learning curve. By seamlessly integrating generative models, ADMET property predictions, searches in vast chemical spaces, and molecular docking, AIDDISON provides a sophisticated platform for modern drug discovery. It enables less computer-savvy scientists to utilize these powerful tools in their daily activities, as demonstrated by an example of identifying a valuable set of molecules for lead optimization. With AIDDISON, the benefits of AI/ML in drug discovery are accessible to all.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Machine Learning Language: En Journal: J Chem Inf Model / J. chem. inf. model / Journal of chemical information and modeling Journal subject: INFORMATICA MEDICA / QUIMICA Year: 2024 Document type: Article Affiliation country: Estados Unidos Country of publication: Estados Unidos

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Machine Learning Language: En Journal: J Chem Inf Model / J. chem. inf. model / Journal of chemical information and modeling Journal subject: INFORMATICA MEDICA / QUIMICA Year: 2024 Document type: Article Affiliation country: Estados Unidos Country of publication: Estados Unidos