TripKa: Accurate and Scalable Acid-Base Dissociation Property Prediction via Triplet Interaction Networks and Physical Knowledge.

Wentao Wei^1,2, Jiahua Rao¹, Bai Xue³, Jiancong Xie¹, Dahao Xu¹, Xichen Sun¹, Yutong Lu¹, Yu Wang², Mingjun Yang³, Yuedong Yang^1,4

¹School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510006, China.

²Peng Cheng Laboratory, Shenzhen, Guangdong 518055, China.

³XtalPi, Shenzhen, Guangdong 518000, China.

⁴Key Laboratory of Machine Intelligence and Advanced Computing (MOE), Sun Yat-sen University, Guangzhou, Guangdong 510006, China.

PubMed DOI

Abstract

The acid-base dissociation constant (pKa) characterizes a molecule's tendency to donate or accept protons, thereby fundamentally influencing its physicochemical properties and behavior. Consequently, pKa is pivotal to diverse applications in drug discovery, including virtual screening and drug design. However, accurate pKa prediction remains challenging, due to intricate atomic interactions among…

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