Abstract 3515: SynAI: An AI-driven cancer drug synergism prediction platform

Abstract

Abstract Introduction: The SynAI solution is an adaptive AI-driven in silico drug synergism screening solution aiming to discover the potential therapeutic value of compounds at an early development stage. Given one of both compound inputs in SMILE sequences, SynAI can predict the potential Bliss score of compounds in any given cell line without the need for compound synthesis or structural analysis.The evaluation version of SynAI is accessible at https://synai.crownbio.com Methods: The AI core of SynAI was constructed using the MLP (multi-layer perceptron) network under the PyTorch machine learning framework. The AI core of SynAI was trained against NCI-Almanac and DrugCombDB datasets. In total, these datasets consist of over 12 million in vitro synergism tests across 150 cancer cell lines of different origins. On average, each cell line is tested against over 6000 two-compound combinations of FDA-approved cancer drugs. One MLP network was trained for each cell line. Essentially, these networks predict the Bliss score for any combination of SMILE-based feature sets known as molecular fingerprints. The networks were trained against the NCI Almanac set and verified against NCI and DrugCombDB using n-fold cross-validation to avoid model overfitting. During the training, a hyperparameter tuning (HT) study was performed for SynAI and other popular algorithms; allowing an objective comparison of SynAI performances. Results: Compared to existing synergism prediction platforms, SynAI yields similar performance in all categories (cf. Table 1) but provides more flexibility for data input using SMILE sequences directly. In addition, the AI core of SynAI can be constantly updated with new inputs from different cell lines and drug combinations. Table 1. Pearson cross correlation (PCC) between measured and predicted Bliss scores of NCI test set Conclusions: Its adaptive and dynamic nature allows SynAI to learn from new data feeds from future studies and has significant potential in reducing the time and cost of synergism screening. PCC Score Cell Lines Algorithm MCF7 OVCAR-8 SK-MEL-5 SynAI 0.68 ± 0.02 0.56 ± 0.07 0.86 ± 0.02 RF 0.64 ± 0.02 0.55 ± 0.03 0.89 ± 0.02 GBX 0.66 ± 0.02 0.48 ± 0.05 0.88 ± 0.02 RNN 0.54 ± 0.12 0.43 ± 0.06 0.83 ± 0.08 Citation Format: Kuan Yan, Runjun Jia, Sheng Guo. SynAI: An AI-driven cancer drug synergism prediction platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3515.