A drug discovery and development pipeline is a prolonged
and complex
process that remains challenging for both computational methods and
medicinal chemists and has not been able to be resolved using computational
methods. Deep learning has been utilized in various fields and achieved
tremendous success in designing novel molecules in the pharmaceutical
industry. Herein, we use state-of-the-art techniques to propose a
deep neural network, AIMLinker, to rapidly design and generate meaningful
drug-like proteolysis targeting chimeras (PROTACs) analogs. The model
extracts the structural information from the input fragments and generates
linkers to incorporate them. We integrate filters in the model to
exclude nondruggable structures guided via protein–protein
complexes while retaining molecules with potent chemical properties.
The novel PROTACs subsequently pass through molecular docking, taking
root-mean-square deviation (RMSD), relative Gibbs free energy (ΔΔGbinding), molecular
dynamics (MD) simulation, and free energy perturbation (FEP) calculations
as the measurement criteria for testing the robustness and feasibility
of the model. The generated novel PROTACs molecules possess similar
structural information with superior binding affinity to the binding
pockets compared to the existing CRBN-dBET6-BRD4 ternary complexes.
We demonstrate the effectiveness of the methodology of leveraging
AIMLinker to design novel compounds for PROTACs molecules exhibiting
better chemical properties compared to the dBET6 crystal pose.