The ultimate goal of drug design is to find novel compounds with desirable pharmacological properties. Designing molecules retaining particular scaffolds as the core structures of the molecules is one of the efficient ways to obtain potential drug candidates with desirable properties. We proposed a scaffold-based molecular generative model for scaffold-based drug discovery, which performs molecule generation based on a wide spectrum of scaffold definitions, including BM-scaffolds, cyclic skeletons, as well as scaffolds with specifications on side-chain properties. The model can generalize the learned chemical rules of adding atoms and bonds to a given scaffold. Furthermore, the generated compounds were evaluated by molecular docking in DRD2 targets and the results demonstrated that this approach can be effectively applied to solve several drug arXiv:1908.07209v4 [q-bio.QM] 5 Sep 2019 design problems, including the generation of compounds containing a given scaffold and de novo drug design of potential drug candidates with specific docking scores.

Alzheimer’s disease (AD) is one of the most challenging diseases around the world with no effective clinical treatment. Previous studies have suggested c-Jun N-terminal kinase 3 (JNK3) as an attractive therapeutic target for AD. Herein, we report 3-substituted indolin-2-one derivatives as the first isoform-selective JNK3 inhibitors by multistage screening. In this study, comparative structure-based virtual screening was performed, and J30-8 was identified with a half-maximal inhibitory concentration of 40 nM, which exhibited over 2500-fold isoform selectivity and marked kinome-wide selectivity. Further study indicated that 1 μM J30-8 exhibited neuroprotective activity in vitro so as to alleviate the spatial memory impairment in vivo through reducing plaque burden and inhibiting the phosphorylation of JNKs, Aβ precursor protein, and Tau protein. All of these indicated J30-8 as proved isoform-selective JNK3 inhibitors that might serve as a useful tool for further JNK3 studies with AD as well as for the development of JNK3 inhibitors for the potential treatment of neurodegenerative diseases.

Molecular fingerprints are the workhorse in ligand-based drug discovery. In recent years, an increasing number of research papers reported fascinating results on using deep neural networks to learn 2D molecular representations as fingerprints. It is anticipated that the integration of deep learning would also contribute to the prosperity of 3D fingerprints. Here, we unprecedentedly introduce deep learning into 3D small molecule fingerprints, presenting a new one we termed as the threedimensional force fields fingerprint (TF3P). TF3P is learned by a deep capsular network whose training is in no need of labeled data sets for specific predictive tasks. TF3P can encode the 3D force fields information of molecules and demonstrates the stronger ability to capture 3D structural changes, to recognize molecules alike in 3D but not in 2D, and to identify similar targets inaccessible by other 2D or 3D fingerprints based on only ligands similarity. Furthermore, TF3P is compatible with both statistical models (e.g., similarity ensemble approach) and machine learning models. Altogether, we report TF3P as a new 3D small molecule fingerprint with a promising future in ligand-based drug discovery. All codes are written in Python and available at https://github.com/canisw/ tf3p.

No abstract

No abstract