Protocol update to: Protocol to generate dual-target compounds using a transformer-based chemical language model

We present a protocol to generate dual-target compounds (DT-CPDs) and triple-target compounds (TT-CPDs) interacting with two and three target proteins, respectively, using transformer-based chemical language models. We describe steps for installing software, preparing data, and pre-training the model on pairs of single-target compounds (ST-CPDs), which bind to an individual protein, and DT-CPDs. We then detail procedures for assembling ST-CPD and corresponding DT- or TT-CPD data for fine-tuning on specific protein pairs or triplets and for evaluating model performance on hold-out test sets. For complete details on the use and execution of this protocol, please refer to Srinivasan et al. 1 for DT-CPDs and to Srinivasan et al. 2 for TT-CPDs. This protocol is an update to Srinivasan et al. 3 • Guidance on the generative design of dual- and triple-target compounds • Steps for applying transformer-based chemical language models • Instructions for model pre-training and fine-tuning Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. We present a protocol to generate dual-target compounds (DT-CPDs) and triple-target compounds (TT-CPDs) interacting with two and three target proteins, respectively, using transformer-based chemical language models. We describe steps for installing software, preparing data, and pre-training the model on pairs of single-target compounds (ST-CPDs), which bind to an individual protein, and DT-CPDs. We then detail procedures for assembling ST-CPD and corresponding DT- or TT-CPD data for fine-tuning on specific protein pairs or triplets and for evaluating model performance on hold-out test sets.