Ligand design for 227Ac extraction by active learning and molecular topology

Abstract

Targeted α-therapy (TAT) is a promising radiotherapeutic technique for the treatment of various cancers due to the high linear energy transfer and low penetration depth of α-particles. Unfortunately, one of the major hindrances in the use of TAT is the accessibility of acceptable α-emitting radioisotopes. Of the acceptable radioisotopes, ²²³Ra, ²²⁴Ra, ²²⁵Ra, and ²²⁵Ac can all originate from ²²⁷Ac. Being able to selectively isolate ²²⁷Ac is crucial for aiding in increasing the accessibility of α-emitting radioisotopes for TAT. Some of the more successful ligands used for the selective separation of trivalent actinides are the 6,6′-bis(1,2,4-triazin-3-yl)-2,2′-bipyridine (BTBP)-based ligand family. Current ligand performance screening is accomplished by using a trial-and-error-based method which is expensive and based primarily on chemical intuition and previous studies. In this study, effective computer-aided ligand screening has been accomplished by generating CyMe₄–BTBP-based ligands and predicting stability constants for ²²⁷Ac extraction of each using scalar relativistic density functional theory (DFT) followed by supervised machine learning (ML). DFT was used to compute stability constants from a 2 : 1 stoichiometric ratio of BTBP to ²²⁷Ac with three nitrate ions for charge balancing as demonstrated by experimental analysis. The computed stability constants coupled with the vectorized information from the optimized BTBP molecular geometries were used for the training of ML workflows. The performance of each algorithm was determined by the validation set and the outcomes compared to the DFT stability constants. This methodology can aid radiochemists in synthesizing targeted ligands for selective isolation of ²²⁷Ac.