Multigram Synthesis of Advanced 6,6-Difluorospiro[3.3]heptane-derived Building Blocks.

A convenient methodology for constructing 6,6-difluorospiro[3.3]heptane scaffold - a conformationally restricted isostere of gem-difluorocycloalkanes - is developed. Alarge array of novel 2-mono- and 2,2-bifunctionalized difluorospiro[3.3]heptane building blocks was obtained through the convergent synthesis strategy using a common synthetic precursor - 1,1-bis(bromomethyl)-3,3-difluorocyclobutane. The target compounds and intermediates were prepared by short reaction sequences (6-10 steps) on multigram scale (up to 0.47 kg).

Synthesis, biological evaluation, and modeling studies of 1,3-disubstituted cyclobutane-containing analogs of combretastatin A4.

With the aim of circumventing the adverse cis/trans-isomerization of combretastatin A4 (CA4), a naturally occurring tumor-vascular disrupting agent, we designed novel CA4 analogs bearing 1,3-cyclobutane moiety instead of the cis-stilbene unit of the parent compound. The corresponding cis and trans cyclobutane-containing derivatives were prepared as pure diastereomers. The structure of the target compounds was confirmed by X-ray diffraction study. The title compounds were evaluated for their cytotoxic properties in human cancer cell lines HepG2 (hepatocarcinoma) and SK-N-DZ (neuroblastoma), and the overall activity was found in micromolar range. Molecular docking studies and molecular dynamics simulation within the colchicine binding site of tubulin were in good agreement with the obtained cytotoxicity data.

Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds.

Deep generative neural networks have been used increasingly in computational chemistry for de novo design of molecules with desired properties. Many deep learning approaches employ reinforcement learning for optimizing the target properties of the generated molecules. However, the success of this approach is often hampered by the problem of sparse rewards as the majority of the generated molecules are expectedly predicted as inactives. We propose several technical innovations to address this problem and improve the balance between exploration and exploitation modes in reinforcement learning. In a proof-of-concept study, we demonstrate the application of the deep generative recurrent neural network architecture enhanced by several proposed technical tricks to design inhibitors of the epidermal growth factor (EGFR) and further experimentally validate their potency. The proposed technical solutions are expected to substantially improve the success rate of finding novel bioactive compounds for specific biological targets using generative and reinforcement learning approaches.