Journal of Computational Chemistry最新文献

The photochemical pathways of acetanilide and paracetamol were investigated using the XMS-CASPT2 quantum chemical method and the cc-pVDZ (correlation consistent polarized valence double- $\zeta$ ) basis set. In both compounds, the bright state is the second excited state, designated as a ${}^1({\mathrm{\pi \pi }}^{*}$ L_a) state. Through a detailed exploration of the potential energy profile and the conical intersection structure between the ${}^1({\mathrm{\pi \pi }}^{*}$ L_a) and ground states, we gained a better understanding of how cleavage might occur in both molecules upon photoexcitation. Other potential relaxation mechanisms, including crossings with the dark ${}^1\left(n{\pi }^{*}\right)$ and ${}^1({\mathrm{\pi \pi }}^{*}$ L_a) states, are also discussed in detail.

The biomolecules interact with their partners in an aqueous media; thus, their solvation energy is an important thermodynamics quantity. In previous works (J. Chem. Theory Comput. 14(2): 1020-1032), we demonstrated that the Poisson-Boltzmann (PB) approach reproduces solvation energy calculated via thermodynamic integration (TI) protocol if the structures of proteins are kept rigid. However, proteins are not rigid bodies and computing their solvation energy must account for their flexibility. Typically, in the framework of PB calculations, this is done by collecting snapshots from molecular dynamics (MD) simulations, computing their solvation energies, and averaging to obtain the ensemble-averaged solvation energy, which is computationally demanding. To reduce the computational cost, we have proposed Gaussian/super-Gaussian-based methods for the dielectric function that use the atomic packing to deliver smooth dielectric function for the entire computational space, the protein and water phase, which allows the ensemble-averaged solvation energy to be computed from a single structure. One of the technical difficulties associated with the smooth dielectric function presentation with respect to polar solvation energy is the absence of a dielectric border between the protein and water where induced charges should be positioned. This motivated the present work, where we report a super-Gaussian regularized Poisson-Boltzmann method and use it for computing the polar solvation energy from single energy minimized structures and assess its ability to reproduce the ensemble-averaged polar solvation on a dataset of 74 high-resolution monomeric proteins.

Graph neural networks (GNN) offer an alternative approach to boost the screening effectiveness in drug discovery. However, their efficacy is often hindered by limited datasets. To address this limitation, we introduced a robust GNN training framework, applied to various chemical databases to identify potent non-nucleoside reverse transcriptase inhibitors (NNRTIs) against the challenging K103N-mutated HIV-1 RT. Leveraging self-supervised learning (SSL) pre-training to tackle data scarcity, we screened 1,824,367 compounds, using multi-step approach that incorporated machine learning (ML)-based screening, analysis of absorption, distribution, metabolism, and excretion (ADME) prediction, drug-likeness properties, and molecular docking. Ultimately, 45 compounds were left as potential candidates with 17 of the compounds were previously identified as NNRTIs, exemplifying the model's efficacy. The remaining 28 compounds are anticipated to be repurposed for new uses. Molecular dynamics (MD) simulations on repurposed candidates unveiled two promising preclinical drugs: one designed against Plasmodium falciparum and the other serving as an antibacterial agent. Both have superior binding affinity compared to anti-HIV drugs. This conceptual framework could be adapted for other disease-specific therapeutics, facilitating the identification of potent compounds effective against both WT and mutants while revealing novel scaffolds for drug design and discovery.