Local Scaffold Diversity-Contributed Generator for Discovering Potential NLRP3 Inhibitors.

Deep generative models have become crucial tools in de novo drug design. In current models for multiobjective optimization in molecular generation, the scaffold diversity is limited when multiple constraints are introduced. To enhance scaffold diversity, we herein propose a local scaffold diversity-contributed generator (LSDC), which can be utilized to generate diverse lead compounds capable of satisfying multiple constraints. Compared to the state-of-the-art methods, molecules generated by LSDC exhibit greater diversity when applied to the generation of inhibitors targeting the NOD-like receptor (NLR) family, pyrin domain-containing protein 3 (NLRP3). We present 12 molecules, some of which feature previously unreported scaffolds, and demonstrate their reasonable docking binding modes. Consequently, the modification of selected scaffolds and subsequent bioactivity evaluation lead to the discovery of two potent NLRP3 inhibitors, A22 and A14, with IC50 values of 38.1 nM and 44.43 nM, respectively. And the oral bioavailability of compound A14 is very high (F is 83.09% in mice). This work contributes to the discovery of novel NLRP3 inhibitors and provides a reference for integrating AI-based generation with wet experiments.

Identification of MISO Hammerstein system using sparse multiple kernel-based hierarchical mixture prior and variational Bayesian inference.

The Hammerstein model is a cascade composition of a static memoryless nonlinear function followed by a linear time-invariant dynamical subsystem, which is capable of modeling a wide range of nonlinear dynamical systems. Model structural parameter selection (including the model order and the nonlinearity order) and sparse representation of the static nonlinear function are two issues that receive increasing interests in Hammerstein system identification. In this paper, a novel Bayesian sparse multiple kernel-based identification method (BSMKM) for multiple-input single-output (MISO) Hammerstein system is proposed to handle those issues, where the basis-function model and the finite impulse response model are used to represent the nonlinear part and the linear part respectively. Firstly, in order to jointly realize the model parameter estimation, the sparse representation of static nonlinear function (the nonlinearity order selection can also be realized indirectly) and the model order selection of linear dynamical system, a hierarchical prior distribution is constructed based on Gaussian scale mixture model and sparse multiple kernel, which can characterize both inter-group sparsity and intra-group correlation structure. Then, a full Bayesian method based on variational Bayesian inference is proposed to estimate all unknown model parameters, including finite impulse response coefficients, hyperparameters and noise variance. Finally, the performance of the proposed BSMKM identification method is evaluated by numerical experiments using both simulation and real data.