Fragment Linker Prediction Using the Deep Encoder-Decoder Network for PROTACs Drug Design.

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.

Harnessing polyhydroxylated pyrrolidines as a stabilizer of acid alpha-glucosidase (GAA) to enhance the efficacy of enzyme replacement therapy in Pompe disease.

To discover small molecules as acid alpha-glucosidase (GAA) stabilizers for potential benefits of the exogenous enzyme treatment toward Pompe disease cells, we started from the initial screening of the unique chemical space, consisting of sixteen stereoisomers of 2-aminomethyl polyhydroxylated pyrrolidines (ADMDPs) to find out two primary stabilizers 17 and 18. Further external or internal structural modifications of 17 and 18 were performed to increase structural diversity, followed by the protein thermal shift study to evaluate the GAA stabilizing ability. Fortunately, pyrrolidine 21, possessing an l-arabino-typed configuration pattern, was identified as a specific potent rh-GAA stabilizer, enabling the suppression of rh-GAA protein denaturation. In a cell-based Pompe model, co-administration of 21 with rh-GAA protein significantly improved enzymatic activity (up to 5-fold) compared to administration of enzyme alone. Potentially, pyrrolidine 21 enables the direct increase of ERT (enzyme replacement therapy) efficacy in cellulo and in vivo.

A Novel Rational PROTACs Design and Validation via AI-Driven Drug Design Approach.

The rational design of novel drug candidates presents a formidable challenge in modern drug discovery. Proteolysis-targeting chimeras (PROTACs) drug design is particularly demanding due to their limited crystal structure availability and design of a viable small molecule to bridge the protein of interest (POI) and ubiquitin-protein ligase (E3). An integrated approach that combines superimposition techniques and deep neural networks is demonstrated in this study to leverage the power of deep learning and structural biology to generate structurally diverse molecules with enhanced binding affinities. The superimposition technique ensures the congruence of initial and new protein-ligand pairs, which are evaluated via subsequent comprehensive screening using the root-mean-square deviation (RMSD), binding free energy (BFE), and buried solvent-accessible surface area (SASA). The final candidates are subjected to the incorporation of molecular dynamics (MD) and free energy perturbation (FEP) simulations to provide a quantitative evaluation of relative binding energies, reinforcing the efficacy and reliability of the generated molecules. The outcomes of the generated novel PROTACs molecules exhibit comparable structural attributes while demonstrating superior binding affinities within the binding pockets when contrasted with those of the established cocrystal ternary complexes. To enhance the generalizability of the workflow, we chose the ternary structure of the cellular inhibitor of apoptosis protein 1 (cIAP1) and Bruton's Tyrosine Kinase (BTK) for validating the chemical properties generated from the processes. The new linker molecules additionally showed superior affinity from the simulations. In summary, this methodology serves as an effective workflow to align computational predictions with current limitations, thereby introducing a novel paradigm in AI-driven drug design.