Artificial intelligence in the life sciences最新文献

PROTACs are a promising therapeutic modality that harnesses the cell’s built-in degradation machinery to degrade specific proteins. Despite their potential, developing new PROTACs is challenging and requires significant domain expertise, time, and cost. Meanwhile, machine learning has transformed drug design and development. In this work, we present a strategy for curating open-source PROTAC data and an open-source deep learning tool for predicting the degradation activity of novel PROTAC molecules. The curated dataset incorporates important information such as $pD{C}_{50}$, $D_{max}$, E3 ligase type, POI amino acid sequence, and experimental cell type. Our model architecture leverages learned embeddings from pretrained machine learning models, in particular for encoding protein sequences and cell type information. We assessed the quality of the curated data and the generalization ability of our model architecture against new PROTACs and targets via three tailored studies, which we recommend other researchers to use in evaluating their degradation activity models. In each study, three models predict protein degradation in a majority vote setting, reaching a top test accuracy of 82.6% and 0.848 ROC AUC, and a test accuracy of 61% and 0.615 ROC AUC when generalizing to novel protein targets. Our results are not only comparable to state-of-the-art models for protein degradation prediction, but also part of an open-source implementation which is easily reproducible and less computationally complex than existing approaches.

Targeted protein degradation (TPD) is a rapidly developing drug discovery technique with unique efficacy and target scope stemming from its degradation-based activity. Molecular glue degraders are a promising arm of TPD, as evidenced by the FDA-approved therapeutics within this class, the increasing number of degraders in clinical development, and their predisposition to drug-likeness. Cereblon (CRBN) glue degraders mediate target degradation by generating a neomorphic interface between CRBN and a protein of interest. While promising, the complicated nature of this CRBN-glue-target ternary complex makes the rational design of molecular glue degraders challenging. For other drug modalities, predictive modeling has been established to leverage existing activity data and generate quantitative structure-activity relationships (QSAR). However, the applicability of QSAR strategies for glues remains under-investigated. Herein, machine learning methodologies were developed to predict glue-mediated recruitment of CRBN to target proteins and achieved promising performance. Generated models leveraged more than a hundred internal screening campaigns across thousands of CRBN glues to predict glue-mediated recruitment of targets to CRBN. Our results show that recruitment activity of CRBN glue degraders can be modeled by machine learning, with 89 % of models producing an area under the receiver operating characteristic curve (ROC AUC) > 0.8 and 70 % of models producing a Matthew's correlation coefficient (MCC) > 0.2 for these primary screening data. Importantly, our findings also indicate that the combination of compound and protein descriptors in the so-called proteochemometric models improves performance, with >80 % of the models exhibiting higher ROC AUC and MCC values than per-target models only based on compound information. Hence, our investigations suggest that proteochemometric modeling is a successful approach for molecular glue degraders. The proposed machine learning strategies can aid compound prioritization based on recruitment efficacy and target selectivity, thus have the potential to facilitate the design and discovery of therapeutic CRBN molecular glues.