Implementation of the Molecular Electrostatic Potential over Graphics Processing Units

CuGBasis: High-performance CUDA/Python library for efficient computation of quantum chemistry density-based descriptors for larger systems

CuGBasis is a free and open-source CUDA®/Python library for efficient computation of scalar, vector, and matrix quantities crucial for the post-processing of electronic structure calculations. CuGBasis integrates high-performance Graphical Processing Unit (GPU) computing with the ease and flexibility of Python programming, making it compatible with a vast ecosystem of libraries. We showcase its utility as a Python library and demonstrate its seamless interoperability with existing Python software to gain chemical insight from quantum chemistry calculations. Leveraging GPU-accelerated code, cuGBasis exhibits remarkable performance, making it highly applicable to larger systems or large databases. Our benchmarks reveal a 100-fold performance gain compared to alternative software packages, including serial/multi-threaded Central Processing Unit and GPU implementations. This paper outlines various features and computational strategies that lead to cuGBasis's enhanced performance, guiding developers of GPU-accelerated code.

Discovery of Potent Selective HDAC6 Inhibitors with 5-Phenyl-1H-indole Fragment: Virtual Screening, Rational Design, and Biological Evaluation

Among the HDACs family, histone deacetylase 6 (HDAC6) has attracted extensive attention due to its unique structure and biological functions. Numerous studies have shown that compared with broad-spectrum HDACs inhibitors, selective HDAC6 inhibitors exert ideal efficacy in tumor treatment with insignificant toxic and side effects, demonstrating promising clinical application prospect. Herein, we carried out rational drug design by integrating a deep learning model, molecular docking, and molecular dynamics simulation technology to construct a virtual screening process. The designed derivatives with 5-phenyl-1H-indole fragment as Cap showed desirable cytotoxicity to the various tumor cell lines, all of which were within 15 μM (ranging from 0.35 to 14.87 μM), among which compound 5i had the best antiproliferative activities against HL-60 (IC50 = 0.35 ± 0.07 μM) and arrested HL-60 cells in the G0/G1 phase. In addition, 5i exhibited better isotype selective inhibitory activities due to the potent potency against HDAC6 (IC50 = 5.16 ± 0.25 nM) and the reduced inhibitory activities against HDAC1 (selective index ≈ 124), which was further verified by immunoblotting results. Moreover, the representative binding conformation of 5i on HDAC6 was revealed and the key residues contributing 5i's binding were also identified via decomposition free-energy analysis. The discovery of lead compound 5i also indicates that virtual screening is still a beneficial tool in drug discovery and can provide more molecular skeletons with research potential for drug design, which is worthy of widespread application.

Using cocrystals as a tool to study non-crystallizing molecules: crystal structure, Hirshfeld surface analysis and computational study of the 1:1 cocrystal of (E)-N-(3,4-difluorophenyl)-1-(pyridin-4-yl)methanimine and acetic acid

Using a 1:1 cocrystal of (E)-N-(3,4-difluorophenyl)-1-(pyridin-4-yl)methanimine with acetic acid, C12H8F2N2·C2H4O2, we investigate the influence of F atoms introduced to the aromatic ring on promoting π-π interactions. The cocrystal crystallizes in the triclinic space group P1. Through crystallographic analysis and computational studies, we reveal the molecular arrangement within this cocrystal, demonstrating the presence of hydrogen bonding between the acetic acid molecule and the pyridyl group, along with π-π interactions between the aromatic rings. Our findings highlight the importance of F atoms in promoting π-π interactions without necessitating full halogenation of the aromatic ring.

Computational tools to study non-covalent interactions and confinement effects in chemical systems

Confinement is a very common phenomenon in chemistry, for example, when molecules are located inside cavities. In these conditions, the electronic structure of atoms and molecules is modified. These changes could be mapped through the interaction with other molecules since non-covalent interactions between molecules are also influenced by confinement. In this work we address both topics, non-covalent interactions, and confined systems, using quantum chemistry tools with new software, emphasizing the importance of analyzing both fields simultaneously.

Design of new molecules against cervical cancer using DFT, theoretical spectroscopy, 2D/3D-QSAR, molecular docking, pharmacophore and ADMET investigations

Cervical cancer is a major health problem of women. Hormone therapy, via aromatase inhibition, has been proposed as a promising way of blocking estrogen production as well as treating the progression of estrogen-dependent cancer. To overcome the challenging complexities of costly drug design, in-silico strategy, integrating Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD), was applied to large representative databases of 39 quinazoline and thioquinazolinone compound derivatives. Quantum chemical and physicochemical descriptors have been investigated using density functional theory (DFT) and MM2 force fields, respectively, to develop 2D-QSAR models, while CoMSIA and CoMFA descriptors were used to build 3D-QSAR models. The robustness and predictive power of the reliable models were verified, via several validation methods, leading to the design of 6 new drug-candidates. Afterwards, 2 ligands were carefully selected using virtual screening methods, taking into account the applicability domain, synthetic accessibility, and Lipinski's criteria. Molecular docking and pharmacophore modelling studies were performed to examine potential interactions with aromatase (PDB ID: 3EQM). Finally, the ADMET properties were investigated in order to select potential drug-candidates against cervical cancer for experimental in vitro and in vivo testing.

A Theoretical Analysis of Interaction Energies and Intermolecular Interactions between Amphotericin B and Potential Bioconjugates Used in the Modification of Nanocarriers for Drug Delivery

Amphotericin B (AmB) is an antibiotic with a wide spectrum of action and low multidrug resistance, although it exhibits self-aggregation, low specificity, and solubility in aqueous media. An alternative for its oral administration is its encapsulation in polymers modified with bioconjugates. The aim of the present computational research is to determine the affinity between AmB and six bioconjugates to define which one could be more suitable. The CAM-B3LYP-D3/6-31+G(d,p) method was used for all computational calculations. The dimerization enthalpy of the most stable and abundant systems at pH = 7 allows obtaining this affinity order: AmB_1,2-distearoyl-sn-glycerol-3-phosphorylethanolamine (DSPE) > AmB_γ-cyclodextrin > AmB_DSPEc > AmB_retinol > AmB_cholesterol > AmB_dodecanol, where DSPEc is a DSPE analog. Quantum theory of atoms in molecules, the non-covalent interactions index, and natural bond orbital analysis revealed the highest abundance of noncovalent interactions for AmB-DSPE (51), about twice the number of interactions of the other dimers. Depending on the interactions’ strength and abundance of the AmB-DSPE dimer, these are classified as strong: O-H---O (2), N-H---O (3) and weak: C-H---O (25), H---H (18), C-H---C (3). Although the C-H---O hydrogen bond is weak, the number of interactions involved in all dimers cannot be underestimated. Thus, non-covalent interactions drive the stabilization of copolymers, and from our analysis, the most promising candidates for encapsulating are DSPE and γ-cyclodextrin.

Non-conventional interactions of N3 inhibitor with the main protease of SARS-CoV and SARS-CoV-2

The extensive spread of COVID-19 in every continent shows that SARS-CoV-2 virus has a higher transmission rate than SARS-CoV virus which emerged in 2002. This results in a global pandemic that is difficult to control. In this investigation, we analyze the interaction of N3 inhibitor and the main protease of SARS-CoV and SARS-CoV-2 by quantum chemistry calculations. Non-covalent interactions involved in these systems were studied using a model of 469 atoms. Density Functional Theory and Quantum Theory of Atoms in Molecules calculations lead us to the conclusion that non-conventional hydrogen bonds are important to describe attractive interactions in these complexes. The energy of these non-conventional hydrogen bonds represents more than a half of the estimated interaction energy for non-covalent contacts. This means that hydrogen bonds are crucial to correctly describe the bonds between inhibitors and the main proteases. These results could be useful for the design of new drugs, since non-covalent interactions are related to possible mechanisms of action of molecules used against these viruses.

Main interactions of dopamine and risperidone with the dopamine D2 receptor

Psychosis is one of the psychiatric disorders that is controlled by dopaminergic drugs such as antipsychotics that have affinity for the dopamine D2 receptor (DRD2). In this investigation we perform quantum chemical calculations of two molecules [dopamine and risperidone] within a large cavity of DRD2 that represents the binding site of the receptor. Dopamine is an endogenous neurotransmitter and risperidone is a second-generation antipsychotic. Non-covalent interactions of dopamine and risperidone with DRD2 are analyzed using the Quantum Theory of Atoms in Molecules (QTAIM) and the Non-Covalent Interaction index (NCI). The QTAIM results show that these molecules strongly interact with the receptor. There are 22 non-covalent interactions for dopamine and 54 for risperidone. The electron density evaluated at each critical binding point is small in both systems but it is higher for dopamine than for risperidone, indicating that the interactions of DRD2 with the first are stronger than with the second molecule. However, the binding energy is higher for risperidone (-72.6 kcal mol-1) than for dopamine (-22.8 kcal mol-1). Thus, the strength of the binding energy is due to the number of contacts rather than the strength of the interactions themselves. This could be related to the ability of risperidone to block DRD2 and may explain the efficacy of this drug for controlling the symptoms of schizophrenia, but likewise its secondary effects.

Non-covalent interactions between sertraline stereoisomers and 2-hydroxypropyl-β-cyclodextrin: a quantum chemistry analysis

Inclusion compounds formed between sertraline stereoisomers and β-cyclodextrin, and 2-hydroxypropyl-β-cyclodextrin, were analyzed by using quantum chemistry methods. The exploration of the potential energy surface was performed using chemical intuition and classical molecular mechanics. This approach delivered around 200 candidates for low energy adducts, which were optimized through the PBE0/6-31G(d,p) method, and after this process solvent effects were considered by the continuous solvent model. This analysis showed that β-cyclodextrin and 2-hydroxypropyl-β-cyclodextrin are good trappers of sertraline, although the isomers suggested by molecular dynamics presented higher binding energies than those obtained by chemical intuition. The role of hydrogen bonds in the formation of adducts was studied using the non-covalent interactions index and the quantum theory of atoms in molecules. In this article we concluded that these interactions are present in all adducts, however, they are not important in the stabilization of these inclusion compounds. The molecular electrostatic potential indicates that Coulomb interactions could be responsible for the formation of these systems, although sophisticated solvent models must be used to confirm this conclusion, which are impractical in this case because of the sizes involved in these systems.

Inclusion compounds formed between sertraline stereoisomers and β-cyclodextrin, and 2-hydroxypropyl-β-cyclodextrin, were analyzed by using quantum chemistry methods.