Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently shown excellent performance in PLI prediction. However, the performance is highly dependent on protein and ligand features utilized for the DNN model. Moreover, in current models, the deciphering of how protein features determine the underlying principles that govern PLI is not trivial. In this work, we developed a DNN framework named SSnet that utilizes secondary structure information of proteins extracted as the curvature and torsion of the protein backbone to predict PLI. We demonstrate the performance of SSnet by comparing against a variety of currently popular machine and non-Machine Learning (ML) models using various metrics. We visualize the intermediate layers of SSnet to show a potential latent space for proteins, in particular to extract structural elements in a protein that the model finds influential for ligand binding, which is one of the key features of SSnet. We observed in our study that SSnet learns information about locations in a protein where a ligand can bind, including binding sites, allosteric sites and cryptic sites, regardless of the conformation used. We further observed that SSnet is not biased to any specific molecular interaction and extracts the protein fold information critical for PLI prediction. Our work forms an important gateway to the general exploration of secondary structure-based Deep Learning (DL), which is not just confined to protein-ligand interactions, and as such will have a large impact on protein research, while being readily accessible for de novo drug designers as a standalone package.

Over the past two decades, the vibrational Stark effect has become an important tool to measure and analyze the in situ electric field strength in various chemical environments with infrared spectroscopy. The underlying assumption of this effect is that the normal stretching mode of a target bond such as CO or CN of a reporter molecule (termed vibrational Stark effect probe) is localized and free from mass-coupling from other internal coordinates, so that its frequency shift directly reflects the influence of the vicinal electric field. However, the validity of this essential assumption has never been assessed. Given the fact that normal modes are generally delocalized because of mass-coupling, this analysis was overdue. Therefore, we carried out a comprehensive evaluation of 68 vibrational Stark effect probes and candidates to quantify the degree to which their target normal vibration of probe bond stretching is decoupled from local vibrations driven by other internal coordinates. The unique tool we used is the local mode analysis originally introduced by Konkoli and Cremer, in particular the decomposition of normal modes into local mode contributions. Based on our results, we recommend 31 polyatomic molecules with localized target bonds as ideal vibrational Stark effect probe candidates.

Palbociclib (PAL) is an effective anti-breast cancer drug, but its use has been partly restricted due to poor bioavailability (resulting from extremely low water solubility) and serious adverse reactions. In this study, two cocrystals of PAL with resorcinol (RES) or orcinol (ORC) were prepared by evaporation crystallization to enhance their solubility. The cocrystals were characterized by single crystal X-ray diffraction, Hirshfeld surface analysis, powder X-ray diffraction, differential scanning calorimetry, thermogravimetric analysis, Fourier transform infrared and scanning electron microscopy. The intrinsic dissolution rates of the PAL cocrystals were determined in three different dissolution media (pH 1.0, pH 4.5 and pH 6.8), and both cocrystals showed improved dissolution rates at pH 1.0 and pH 6.8 in comparison to the parent drug. In addition, the cocrystals increased the solubility of PAL at pH 6.8 by 2–3 times and showed good stabilities in both the accelerated stability testing and stress testing. The PAL-RES cocrystal also exhibited an improved relative bioavailability (1.24 times) than PAL in vivo pharmacokinetics in rats. Moreover, the in vitro cytotoxicity assay of PAL-RES showed an increased IC₅₀ value for normal cells, suggesting a better biosafety profile than PAL. Co-crystallization may represent a promising strategy for improving the physicochemical properties of PAL with better pharmacokinetics.

Periodic local vibrational modes were calculated with the rev-vdW-DF2 density functional to quantify the intrinsic strength of the X-I⋯OA-type halogen bonding (X = I or Cl; OA: carbonyl, ether and N-oxide groups) in 32 model systems originating from 20 molecular crystals. We found that the halogen bonding between the donor dihalogen X-I and the wide collection of acceptor molecules OA features considerable variations of the local stretching force constants (0.1–0.8 mdyn/Å) for I⋯O halogen bonds, demonstrating its powerful tunability in bond strength. Strong correlations between bond length and local stretching force constant were observed in crystals for both the donor X-I bonds and I⋯O halogen bonds, extending for the first time the generalized Badger’s rule to crystals. It is demonstrated that the halogen atom X controlling the electrostatic attraction between the $\sigma$ -hole on atom I and the acceptor atom O dominates the intrinsic strength of I⋯O halogen bonds. Different oxygen-containing acceptor molecules OA and even subtle changes induced by substituents can tweak the $n\to {\sigma }^{\ast }$ (X-I) charge transfer character, which is the second important factor determining the I⋯O bond strength. In addition, the presence of the second halogen bond with atom X of the donor X-I bond in crystals can substantially weaken the target I⋯O halogen bond. In summary, this study performing the in situ measurement of halogen bonding strength in crystalline structures demonstrates the vast potential of the periodic local vibrational mode theory for characterizing and understanding non-covalent interactions in materials.

The unified reaction valley approach (URVA) differs from mainstream mechanistic studies, as it describes a chemical reaction via the reaction path and the surrounding reaction valley on the potential energy surface from the van der Waals region to the transition state and far out into the exit channel, where the products are located. The key feature of URVA is the focus on the curving of the reaction path. Moving along the reaction path, any electronic structure change of the reacting molecules is registered by a change in their normal vibrational modes and their coupling with the path, which recovers the curvature of the reaction path. This leads to a unique curvature profile for each chemical reaction with curvature minima reflecting minimal change and curvature maxima, the location of important chemical events such as bond breaking/forming, charge polarization and transfer, rehybridization, etc. A unique decomposition of the path curvature into internal coordinate components provides comprehensive insights into the origins of the chemical changes taking place. After presenting the theoretical background of URVA, we discuss its application to four diverse catalytic processes: (i) the Rh catalyzed methanol carbonylation—the Monsanto process; (ii) the Sharpless epoxidation of allylic alcohols—transition to heterogenous catalysis; (iii) Au(I) assisted [3,3]-sigmatropic rearrangement of allyl acetate; and (iv) the Bacillus subtilis chorismate mutase catalyzed Claisen rearrangement—and show how URVA leads to a new protocol for fine-tuning of existing catalysts and the design of new efficient and eco-friendly catalysts. At the end of this article the pURVA software is introduced. The overall goal of this article is to introduce to the chemical community a new protocol for fine-tuning existing catalytic reactions while aiding in the design of modern and environmentally friendly catalysts.

11 aryl–lone pair and three aryl–anion $\pi$ –hole interactions are investigated, along with the argon–benzene dimer and water dimer as reference compounds, utilizing the local vibrational mode theory, originally introduced by Konkoli and Cremer, to quantify the strength of the $\pi$ –hole interaction in terms of a new local vibrational mode stretching force constant between the two engaged monomers, which can be conveniently used to compare different $\pi$ –hole systems. Several factors have emerged which influence strength of the $\pi$ –hole interactions, including aryl substituent effects, the chemical nature of atoms composing the aryl rings/ $\pi$ –hole acceptors, and secondary bonding interactions between donors/acceptors. Substituent effects indirectly affect the $\pi$ –hole interaction strength, where electronegative aryl-substituents moderately increase $\pi$ –hole interaction strength. N-aryl members significantly increase $\pi$ –hole interaction strength, and anion acceptors bind more strongly with the $\pi$ –hole compared to charge neutral acceptors (lone–pair donors). Secondary bonding interactions between the acceptor and the atoms in the aryl ring can increase $\pi$ –hole interaction strength, while hydrogen bonding between the $\pi$ –hole acceptor/donor can significantly increase or decrease strength of the $\pi$ –hole interaction depending on the directionality of hydrogen bond donation. Work is in progress expanding this research on aryl $\pi$ –hole interactions to a large number of systems, including halides, CO, and OCH₃⁻ as acceptors, in order to derive a general design protocol for new members of this interesting class of compounds.