Journal of Computational Chemistry最新文献

In the realm of artificial intelligence-driven drug discovery (AIDD), accurately predicting the influence of molecular structures on their properties is a critical research focus. While deep learning models based on graph neural networks (GNNs) have made significant advancements in this area, prior studies have primarily concentrated on molecule-level representations, often neglecting the impact of functional group structures and the potential relationships between fragments on molecular property predictions. To address this gap, we introduce the multi-scale feature attention graph neural network (MfGNN), which enhances traditional atom-based molecular graph representations by incorporating fragment-level representations derived from chemically synthesizable BRICS fragments. MfGNN not only effectively captures both the structural information of molecules and the features of functional groups but also pays special attention to the potential relationships between fragments, exploring how they collectively influence molecular properties. This model integrates two core mechanisms: a graph attention mechanism that captures embeddings of molecules and functional groups, and a feature extraction module that systematically processes BRICS fragment-level features to uncover relationships among the fragments. Our comprehensive experiments demonstrate that MfGNN outperforms leading machine learning and deep learning models, achieving state-of-the-art performance in 8 out of 11 learning tasks across various domains, including physical chemistry, biophysics, physiology, and toxicology. Furthermore, ablation studies reveal that the integration of multi-scale feature information and the feature extraction module enhances the richness of molecular features, thereby improving the model's predictive capabilities.

In this work, the stability, aromaticity and radical character of pristine and eleven BN-doped armchair 5 and zigzag 5, 6, and 7 periacenes, were chosen for studying the effect of different doping schemes to stabilize the periacene, and to direct the open-shell density into specific regions of the PAH sheets. Ab initio multireference methods and different DFT functionals were used to analyze the singlet triplet (ST) energy. Moreover, a range of descriptors were used to characterize the open-shell character and aromaticity of the different doped structures. The overall results provide a good overview of the efficiency of the different doping topologies. In general, because of the closed-shell character of the borazine doping units, the role of the doping is to reduce the strong open-shell character of the pristine periacenes. Substitutions along the zigzag edges has a significant effect while doping along the armchair edges is not significant.

The G protein-coupled receptor (GPCR) pharmacology accounts for a significant field in research, clinical studies, and therapeutics. Computer-aided drug discovery is an evolving suite of techniques and methodologies that facilitate accelerated progress in drug discovery and repositioning. However, the structure–activity relationships of molecules targeting GPCRs are highly challenging in many cases since slight structural modifications can lead to drastic changes in biological functionality. Numerous molecular descriptors have been described, many of which successfully characterize the structural and physicochemical features of drug sets. Nonetheless, elucidating the structure–functionality relationships over extensive sets of drugs with multiple structural variations and known biological activity remains challenging in various biological systems. This work presents novel topological descriptors using Laplacian matrices, weighted, and scaled by atomic mass and partial charges. We tested these descriptors on three sets of GPCR ligands: muscarinic, β-adrenergic, and δ-opioid receptor ligands, evaluating their potential as functional descriptors of these receptors.

The suitability of a range of interatomic potentials for elemental tin was evaluated in order to identify an appropriate potential for modeling the stanene (2D tin) allotropes. Structural and mechanical features of the flat (F), low-buckled (LB), high-buckled (HB), full dumbbell (FD), trigonal dumbbell (TD), honeycomb dumbbell (HD), and large honeycomb dumbbell (LHD) monolayer tin (stanene) phases, were gained by means of the density functional theory (DFT) and molecular statics (MS) calculations with ten different Tersoff, modified embedded atom method (MEAM), and machine-learning-based (ML-IAP) interatomic potentials. A systematic quantitative comparison and discussion of the results is reported.

Various electronically excited states and the feasibility of direct laser cooling of SH, SeH, and TeH are investigated using the highly accurate ab initio and dynamical methods. For the detailed calculations of the seven low-lying Λ-S states of SH, we utilized the internally contracted multireference configuration interaction approach, considering the spin-orbit coupling (SOC) effects. Our calculated spectroscopic constants are in very good agreement with the available experimental results. It is found that, from SH to TeH, the crossing points among the A²Σ⁺ and three electronically excited states gradually shift downward toward the ground vibrational level of the A²Σ⁺ state. This is consistent with our previous findings in other molecular systems and makes the laser cooling of TeH unfeasible. Our calculations indicate that the three crossing points, respectively, between the A²Σ⁺ and a⁴Σ⁻, A²Σ⁺ and B²Σ⁻, and A²Σ⁺ and b⁴Π states of SH, all lie above the v' = 1 vibrational level of the A²Σ⁺ state, as a result of which the crossings involving electronic states of higher energy would not hinder its laser cooling. Based upon our study on various excited states, we have constructed a viable laser-cooling scheme for SH, utilizing three laser beams and leveraging the A²Σ⁺ → X²Π transition. This transition possesses a very large vibrational branching ratio R₀₀ (0.9558), an abundant number of scattered photons (9.30 × 10³), and a short radiative lifetime (787 ns). Our work underscores the important role of excited-state crossings in molecular laser cooling and demonstrates that SH emerges as a very good candidate for ultracold molecules.

Lanthanide (Ln³⁺) tetrakis complexes, C[Ln(L)₄], are important for applications due to their high quantum yields, solubility, and stability. Their luminescent properties depend on the structure, particularly the coordination polyhedron, the assessment of computational methods for calculating their structures is paramount. Usually, this assessment uses the RMSD of distances in the [Ln(L)₄]⁻ complex or {LnO₈} polyhedron between crystallographic and calculated structures. However, since ligand field (LF) splitting is highly geometry-dependent, the RMSD between experimental LF splitting and Stark levels (RMSD-LF) offers a more accurate measure for evaluating quantum methods. LF energy eigenvalues were calculated using the simple overlap model (SOM), with geometries optimized by various density functionals. M06 and M06-L functionals, with def2-SVP/MWB52(Eu)/CPCM, demonstrate the best balance in best accuracy and low computational cost, making them suitable for modeling C[Eu(L)₄] complexes.