针对选择性靶向大麻素受体亚型2（CB2R）的小分子化合物的开发逐渐成为治疗神经退行性疾病，以及抑制肿瘤发生和进展的有趣治疗策略。在这一背景下，能够预测CB2R与亚型1（CB1R）的亲和力和选择性的计算工具非常理想，因为CB1R的调节与不良的精神活性效应有关。在本研究中，我们开发了一系列基于高质量生物活性数据的机器学习分类器，这些数据来自于从ChEMBL v30中提取的作用于CB2R和/或CB1R的小分子化合物。我们的分类器在准确确定CB2R亲和力、CB1R亲和力和CB2R/CB1R选择性方面表现出很强的预测能力。在构建的模型中，使用随机森林算法得到的模型被证明是最优秀的模型之一（验证时的AUC≥0.96），并通过特别开发的用户友好的网络平台ALPACA（https://www.ba.ic.cnr.it/softwareic/alpaca/）免费提供。由于其用户友好的界面和强大的预测能力，ALPACA可以成为设计选择性CB2R调节剂过程中节省时间和资源的宝贵工具。版权© 2023 作者。由Elsevier Ltd.出版保留所有权利。

The development of small molecules that selectively target the cannabinoid receptor subtype 2 (CB2R) is emerging as an intriguing therapeutic strategy to treat neurodegeneration, as well as to contrast the onset and progression of cancer. In this context, in-silico tools able to predict CB2R affinity and selectivity with respect to the subtype 1 (CB1R), whose modulation is responsible for undesired psychotropic effects, are highly desirable. In this work, we developed a series of machine learning classifiers trained on high-quality bioactivity data of small molecules acting on CB2R and/or CB1R extracted from ChEMBL v30. Our classifiers showed strong predictive power in accurately determining CB2R affinity, CB1R affinity, and CB2R/CB1R selectivity. Among the built models, those obtained using random forest as algorithm proved to be the top-performing ones (AUC in validation ≥0.96) and were made freely accessible through a user-friendly web platform developed ad hoc and called ALPACA (https://www.ba.ic.cnr.it/softwareic/alpaca/). Due to its user-friendly interface and robust predictive power, ALPACA can be a valuable tool in saving both time and resources involved in the design of selective CB2R modulators.Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.