Niranjan, V.; Uttarkar, A.; Ramakrishnan, A.; Muralidharan, A.; Shashidhara, A.; Acharya, A.; Tarani, A.; Kumar, J.
De Novo Design of Anti-COVID Drugs Using Machine Learning-Based Equivariant Diffusion Model Targeting the Spike Protein. Curr. Issues Mol. Biol. 2023, 45, 4261-4284.
https://doi.org/10.3390/cimb45050271
AMA Style
Niranjan V, Uttarkar A, Ramakrishnan A, Muralidharan A, Shashidhara A, Acharya A, Tarani A, Kumar J.
De Novo Design of Anti-COVID Drugs Using Machine Learning-Based Equivariant Diffusion Model Targeting the Spike Protein. Current Issues in Molecular Biology. 2023; 45(5):4261-4284.
https://doi.org/10.3390/cimb45050271
Chicago/Turabian Style
Niranjan, Vidya, Akshay Uttarkar, Ananya Ramakrishnan, Anagha Muralidharan, Abhay Shashidhara, Anushri Acharya, Avila Tarani, and Jitendra Kumar.
2023. "De Novo Design of Anti-COVID Drugs Using Machine Learning-Based Equivariant Diffusion Model Targeting the Spike Protein" Current Issues in Molecular Biology 45, no. 5: 4261-4284.
https://doi.org/10.3390/cimb45050271
APA Style
Niranjan, V., Uttarkar, A., Ramakrishnan, A., Muralidharan, A., Shashidhara, A., Acharya, A., Tarani, A., & Kumar, J.
(2023). De Novo Design of Anti-COVID Drugs Using Machine Learning-Based Equivariant Diffusion Model Targeting the Spike Protein. Current Issues in Molecular Biology, 45(5), 4261-4284.
https://doi.org/10.3390/cimb45050271
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