Hydrogen bond dynamics and conformational flexibility in antipsychotics

Insilico generation of novel ligands for the inhibition of SARS-CoV-2 main protease (3CLpro) using deep learning

The recent emergence of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing the coronavirus disease (COVID-19) has become a global public health crisis, and a crucial need exists for rapid identification and development of novel therapeutic interventions. In this study, a recurrent neural network (RNN) is trained and optimized to produce novel ligands that could serve as potential inhibitors to the SARS-CoV-2 viral protease: 3 chymotrypsin-like protease (3CL^pro). Structure-based virtual screening was performed through molecular docking, ADMET profiling, and predictions of various molecular properties were done to evaluate the toxicity and drug-likeness of the generated novel ligands. The properties of the generated ligands were also compared with current drugs under various phases of clinical trials to assess the efficacy of the novel ligands. Twenty novel ligands were selected that exhibited good drug-likeness properties, with most ligands conforming to Lipinski's rule of 5, high binding affinity (highest binding affinity: -9.4 kcal/mol), and promising ADMET profile. Additionally, the generated ligands complexed with 3CL^pro were found to be stable based on the results of molecular dynamics simulation studies conducted over a 100 ns period. Overall, the findings offer a promising avenue for the rapid identification and development of effective therapeutic interventions to treat COVID-19.

Ammonia Storage in Hydrogen Bond-Rich Microporous Polymers

The fascinating structural flexibility of porous polymers is highly attractive because it can result in optimized materials with specific host-guest interactions. Nevertheless, the fundamental mechanisms responsible for controlling the weak interactions of these hydrogen bond-rich networks-essential for developing smart task-specific materials used in recognition, capture, and sequestration processes-remain unexplored. Herein, by systematically comparing performance changes between poly(amic acid) (PAA)- and polycyclic imide (PI)-based porous polymers before and after NH₃ adsorption, the role of hydrogen bonds in conformational lability and responsiveness toward guest molecules is highlighted. By combining thermal gravimetric analysis with neutron spectroscopy supported by DFT calculations, we demonstrate that PAA's chemical and physical stability is enhanced by the presence of stronger host-guest interactions. This observation also emphasizes the idea that efficient adsorption relies on having a high number of sites, upon which gas molecules can adsorb with greater affinity via strong hydrogen bonding interactions.