TY - GEN
T1 - SPARK
T2 - 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
AU - Kwon, Bum Chul
AU - Rabinovici-Cohen, Simona
AU - Moturi, Beldine
AU - Mwaura, Ruth
AU - Wahome, Kezia
AU - Njeru, Oliver
AU - Shinyenyi, Miguel
AU - Wanjiru, Catherine
AU - Remy, Sekou
AU - Ogallo, William
AU - Guez, Itai
AU - Suryanarayanan, Partha
AU - Morrone, Joseph
AU - Sethi, Shreyans
AU - Kang, Seung Gu
AU - Huynh, Tien
AU - Ng, Kenney
AU - Mahajan, Diwakar
AU - Li, Hongyang
AU - Ninio, Matan
AU - Ayati, Shervin
AU - Hexter, Efrat
AU - Cornell, Wendy
N1 - Publisher Copyright:
© 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2024
Y1 - 2024
N2 - Biomedical foundation models, trained on diverse sources of small molecule data, hold great potential for accelerating drug discovery. However, their complex nature often presents a barrier for researchers seeking scientific insights and drug candidate generation. SPARK addresses this challenge by providing a user-friendly, web-based interface that empowers researchers to leverage these powerful models in their scientific workflows. Through SPARK, users can specify target proteins and desired molecule properties, adjust pre-trained models for tailored inferences, generate lists of potential drug candidates, analyze and compare molecules through interactive visualizations, and filter candidates based on key metrics (e.g., toxicity). By seamlessly integrating human knowledge and biomedical AI models' capabilities through an interactive web-based system, SPARK can improve the efficiency of collaboration between human experts and AI, thereby accelerating drug candidate discovery and ultimately leading to breakthroughs in finding cures for various diseases.
AB - Biomedical foundation models, trained on diverse sources of small molecule data, hold great potential for accelerating drug discovery. However, their complex nature often presents a barrier for researchers seeking scientific insights and drug candidate generation. SPARK addresses this challenge by providing a user-friendly, web-based interface that empowers researchers to leverage these powerful models in their scientific workflows. Through SPARK, users can specify target proteins and desired molecule properties, adjust pre-trained models for tailored inferences, generate lists of potential drug candidates, analyze and compare molecules through interactive visualizations, and filter candidates based on key metrics (e.g., toxicity). By seamlessly integrating human knowledge and biomedical AI models' capabilities through an interactive web-based system, SPARK can improve the efficiency of collaboration between human experts and AI, thereby accelerating drug candidate discovery and ultimately leading to breakthroughs in finding cures for various diseases.
UR - https://www.scopus.com/pages/publications/85204304953
M3 - Conference contribution
AN - SCOPUS:85204304953
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 8713
EP - 8716
BT - Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
A2 - Larson, Kate
PB - International Joint Conferences on Artificial Intelligence
Y2 - 3 August 2024 through 9 August 2024
ER -