Abstract
A multitargeted therapeutic approach with hybrid drugs is a promising strategy to enhance anticancer efficiency and overcome drug resistance in nonsmall cell lung cancer (NSCLC) treatment. Estimating affinities of small molecules against targets of interest typically proceeds as a preliminary action for recent drug discovery in the pharmaceutical industry. In this investigation, we employed machine learning models to provide a computationally affordable means for computer-aided screening to accelerate the discovery of potential drug compounds. In particular, we introduced a quantitative structure–activity-relationship (QSAR)-based multitask learning model to facilitate an in silico screening system of multitargeted drug development. Our method combines a recently developed graph-based neural network architecture, principal neighborhood aggregation (PNA), with a descriptor-based deep neural network supporting synergistic utilization of molecular graph and fingerprint features. The model was generated by more than ten-thousands affinity-reported ligands of seven crucial receptor tyrosine kinases in NSCLC from two public data sources. As a result, our multitask model demonstrated better performance than all other benchmark models, as well as achieving satisfying predictive ability regarding applicable QSAR criteria for most tasks within the model’s applicability. Since our model could potentially be a screening tool for practical use, we have provided a model implementation platform with a tutorial that is freely accessible hence, advising the first move in a long journey of cancer drug development.
Highlights
Multitargeted therapies by hybrid drugs as multitargeted agents with a concept of “single molecule multiple targets” have been alternatively introduced to overcome the anticancer drug resistance together with improving their effectiveness and safety issues [1,2,3,4]
To enable the discovery of hits interacting with multiple targets in conventional quantitative structure–activity relationship (QSAR), models are necessarily performed in sequences to filter chemicals in a library by each desired target criteria leading to a limited number of hits passing through the final filter [23]
As the activity overlap was found in data distribution, the multitask learning method was applied on the principal neighborhood aggregation (PNA)+deep neural networks (DNN) model and the results show that the all-target root-mean-square error (RMSE) is reduced to 0.5883
Summary
Multitargeted therapies by hybrid drugs as multitargeted agents with a concept of “single molecule multiple targets” have been alternatively introduced to overcome the anticancer drug resistance together with improving their effectiveness and safety issues [1,2,3,4]. In the case of nonsmall cell lung cancer (NSCLC) medication, the US Food and Drug Administration (FDA), far, approved some receptor tyrosine kinase inhibitors associated with multitargeted affinity, for instance, ErbB family inhibitor Afatinib, ALK/RET dual-blocker Alectinib, and ALK/MET/ROS1 multi-inhibitor Crizotinib [6,7]. These multitargeted drugs were reported in positive outcomes resulting in more prolonged progression-free survival and reduced lung cancer symptoms [8,9,10,11] even though most of them were not initially designed for the border target interaction [12,13]. Multitask QSAR modeling would be an applicable assistant tool [25] to accelerate the discovery of lead candidates targeting multiple tyrosine kinases for NSCLC treatment
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