Caryl Ann Capao, Bessa Nicole Tamarra
Snakebite envenoming is a potentially life- threatening disease caused by toxins from the bite of a venomous snake. Envenoming can also be caused by having venom sprayed into the eyes by certain species of snakes, such as the Naja sumatrana, these venomous snakes have the ability to spit venom as a defense measure. Communities from rural and agricultural areas in the Philippines are at high-risk towards the exposure of venomous snakes. However, the venom structures of Naja sumatrana, Naja philippinensis, Naja samarensis, and the Philippine population of Ophiophagus hannah are still poorly studied. This paper describes the utilization of artificial intelligence to simulate the venom structures of Naja sumatrana, Naja philippinensis, Naja samarensis, and the Philippine population of Ophiophagus hannah. Comparative analysis of the venom structures is also conducted to determine their toxin similarity. An understanding of the venom structure is important to determine the effectiveness of the available antivenom in the Philippines, as well as the medical potential of these venoms. By analyzing venom protein sequences, the deep learning model can predict the three-dimensional structures of venom toxins. © 2025 IEEE.
University of San Carlos, Department of Computer Information Science and Mathematics, Cebu, Consolacion, Philippines; University of San Carlos, Department of Computer Information Science and Mathematics, Talamban, Cebu, Philippines
