Rovenchak, Andrij; Druchok, Maksym
doi: 10.1002/jcc.27292pmid: 38174834
Design of new drugs is a challenging process: a candidate molecule should satisfy multiple conditions to act properly and make the least side‐effect—perfect candidates selectively attach to and influence only targets, leaving off‐targets intact. The amount of experimental data about various properties of molecules constantly grows, promoting data‐driven approaches. However, the applicability of typical predictive machine learning techniques can be substantially limited by a lack of experimental data about a particular target. For example, there are many known Thrombin inhibitors (acting as anticoagulants), but a very limited number of known Protein C inhibitors (coagulants). In this study, we present our approach to suggest new inhibitor candidates by building an effective representation of chemical space. For this aim, we developed a deep learning model—autoencoder, trained on a large set of molecules in the SMILES format to map the chemical space. Further, we applied different sampling strategies to generate novel coagulant candidates. Symmetrically, we tested our approach on anticoagulant candidates, where we were able to predict their inhibition towards Thrombin. We also compare our approach with MegaMolBART—another deep learning generative model, but exploiting similar principles of navigation in a chemical space.
Chuntakaruk, Hathaichanok; Boonpalit, Kajjana; Kinchagawat, Jiramet; Nakarin, Fahsai; Khotavivattana, Tanatorn; Aonbangkhen, Chanat; Shigeta, Yasuteru; Hengphasatporn, Kowit; Nutanong, Sarana; Rungrotmongkol, Thanyada; Hannongbua, Supot
Ibrahim, Mohammad Taha I.; Alatoom, Dunia; Furtenbacher, Tibor; Császár, Attila G.; Yurchenko, Sergei N.; Azzam, Ala'a A. A.; Tennyson, Jonathan
doi: 10.1002/jcc.27266pmid: 38189163
A set of empirical rovibrational energy levels, obtained through the MARVEL (measured active rotational‐vibrational energy levels) procedure, is presented for the 13C 16O 2 isotopologue of carbon dioxide. This procedure begins with the collection and analysis of experimental rovibrational transitions from the literature, allowing for a comprehensive review of the literature on the high‐resolution spectroscopy of 13C 16O 2, which is also presented. A total of 60 sources out of more than 750 checked provided 14,101 uniquely measured and assigned rovibrational transitions in the wavenumber range of 579–13,735 cm −1. This is followed by a weighted least‐squares refinement yielding the energy levels of the states involved in the measured transitions. Altogether 6318 empirical rovibrational energies have been determined for 13C 16O 2. Finally, estimates have been given for the uncertainties of the empirical energies, based on the experimental uncertainties of the transitions. The detailed analysis of the lines and the spectroscopic network built from them, as well as the uncertainty estimates, all serve to pinpoint possible errors in the experimental data, such as typos, misassignment of quantum numbers, and misidentifications. Errors found in the literature data were corrected before including them in the final MARVEL dataset and analysis.
Tang, Carson L.; Heide, Alexander G.; Heide, Alexandra D.; Douberly, Gary E.; Turney, Justin M.; Schaefer, Henry F.
doi: 10.1002/jcc.27293pmid: 38197269
Thallium chemistry is experiencing unprecedented importance. Therefore, it is valuable to characterize some of the simplest thallium compounds. Stationary points along the singlet and triplet Tl 2H 2 potential energy surface have been characterized. Stationary point geometries were optimized with the CCSD(T)/aug‐cc‐pwCVQZ‐PP method. Harmonic vibrational frequencies were computed at the same level of theory while anharmonic vibrational frequencies were computed at the CCSD(T)/aug‐cc‐pwCVTZ‐PP level of theory. Final energetics were obtained with the CCSDT(Q) method. Basis sets up to augmented quintuple‐zeta cardinality (aug‐cc‐pwCV5Z‐PP) were employed to obtain energetics in order to extrapolate to the complete basis set limits using the focal point approach. Zero‐point vibrational energy corrections were appended to the extrapolated energies in order to determine relative energies at 0 K. It was found that the planar dibridged isomer lies lowest in energy while the linear structure lies highest in energy. The results were compared to other group 13 M 2H 2 (M = B, Al, Ga, In, and Tl) theoretical studies and some interesting variations are found. With respect to experiment, incompatibilities exist.
Rodríguez‐Mayorga, M.; Besalú‐Sala, P.; Pérez‐Jiménez, Á. J.; Sancho‐García, J. C.
doi: 10.1002/jcc.27302pmid: 38206899
The effective calculation of static nonlinear optical properties requires a considerably high accuracy at a reasonable computational cost, to tackle challenging organic and inorganic systems acting as precursors and/or active layers of materials in (nano‐)devices. That trade‐off implies to obtain very accurate electronic energies in the presence of externally applied electric fields to consequently obtain static polarizabilities (αij) and hyper‐polarizabilities (βijk and γijkl). Density functional theory is known to provide an excellent compromise between accuracy and computational cost, which is however largely impeded for these properties without introducing range‐separation techniques. We thus explore here the ability of a modern (double‐hybrid and range‐separated) Range‐Separated eXchange Quadratic Integrand Double‐Hybrid exchange‐correlation functional to compete in accuracy with more costly and/or tuned methods, thanks to its robust and parameter‐free nature.
Silva, Lucas de S.; Colherinhas, Guilherme; Cardoso, Wesley B.
doi: 10.1002/jcc.27303pmid: 38206886
In this article, we employed concepts from Density Functional Theory to investigate the interaction energy behavior between the fragments of two‐dimensional systems composed of graphene‐based materials and lithium ions. Specifically, the proposed system consists of two graphene sheets separated by a controlled distance (face‐to‐face), with a lithium ion positioned at the center of this separation. Additionally, we examined potential electronic transitions within these systems and assessed the feasibility of quantum entanglement generation and manipulation. Our findings revealed that the interaction energies within the analyzed systems exhibited behavior akin to that described by the Lennard‐Jones potential, which characterizes systems with favorable energy for their formation. The results further yielded estimates for the constants ϵ and σ, with values of −66.59 kcal/mol and 1.63 Å, respectively. Specific electronic transitions were identified, suggesting the potential for quantum entanglement generation and manipulation among the two‐dimensional graphene system mediated by the lithium ion interactions.
Lakra, Sangeeta; Mukherjee, S. K.
doi: 10.1002/jcc.27308pmid: 38205659
The structural, optoelectronics, and transport properties of TlTaO3 compounds were determined utilizing the full potential augmented plane wave approach using first‐principle method. We have considered the generalized gradient approximation for structural optimization and modified Becke–Johnson for electronic properties. The electronic properties reveal that the studied TlTaO3 possesses direct bandgap of magnitude 1.52 eV. Between 0 and 12 eV, optical spectra calculations are made, taking into account the real and imaginary parts of the dielectric function, refractive index, and loss function. The transport properties are estimated considering Boltzmann transport theory. The Seebeck coefficient, electrical conductivity, thermal conductivity, and power factor are all assessed using the Boltzmann transport theory. The optimized thermoelectric response of the examined TlTaO3 is produced by the improved carrier mobility, which also improves the thermoelectric efficiency of the TlTaO3. The obtained results will act as a theoretical road map for upcoming experimental and commercial TlTaO3 applications.
Mehta, Nisha; Martin, Jan M. L.
doi: 10.1002/jcc.27294pmid: 38216516
Partial charges are a central concept in general chemistry and chemical biology, yet dozens of different computational definitions exist. In prior work [Cho et al., ChemPhysChem 21, 688‐696 (2020)], we showed that these can be reduced to at most three ‘principal components of ionicity’. The present study addressed the dependence of computed partial charges q on 1‐particle basis set and (for WFT methods) n‐particle correlation treatment or (for DFT methods) exchange‐correlation functional, for several representative partial charge definitions such as QTAIM, Hirshfeld, Hirshfeld‐I, HLY (electrostatic), NPA, and GAPT. Our findings show that semi‐empirical double hybrids can closely approach the CCSD(T) ‘gold standard’ for this property. In fact, owing to an error compensation in MP2, CCSD partial charges are further away from CCSD(T) than is MP2. The nonlocal correlation is important, especially when there is a substantial amount of nonlocal exchange. Employing range separation proves to be “mostly" not advantageous, while global hybrids perform optimally for 20%–30% Hartree‐Fock exchange across all charge types. Basis set convergence analysis shows that an augmented triple‐zeta heavy‐aug‐cc‐pV(T+d)Z basis set or a partially augmented jun‐cc‐pV(T+d)Z basis set is sufficient for Hirshfeld, Hirshfeld‐I, HLY, and GAPT charges. In contrast, QTAIM and NPA display slower basis set convergence. It is noteworthy that for both NPA and QTAIM, HF exhibits markedly slower basis set convergence than the correlation components of MP2 and CCSD. Triples corrections in CCSD(T), denoted as CCSD(T)‐CCSD, exhibit even faster basis set convergence.
Filatov, Michael; Mironov, Vladimir; Kraka, Elfi
doi: 10.1002/jcc.27304pmid: 38216513
The photophysical properties of a series of recently synthesized single benzene fluorophores were investigated using ensemble density functional theory calculations. The energetic stability of the ground and excited state species were counterposed against the aromaticity index derived from local vibrational modes. It was found that the large Stokes shift of the fluorophores (up to ca. 5800 cm −1) originates from the effect of electron donating and electron withdrawing substituents rather than π‐delocalization and related (anti‐)aromaticity. On the basis of nonadiabatic molecular dynamics simulations, the absence of fluorescence from one of the regioisomers was explained by the occurrence of easily accessible S 1/S 0 conical intersections below the vertical excitation energy level. It is demonstrated in the manuscript that the analysis of local mode force constants and the related aromaticity index represent a useful tool for the characterization of π‐delocalization effects in π‐conjugated compounds.
Showing 1 to 10 of 11 Articles
doi: 10.1002/jcc.27298pmid: 38174739
In the pursuit of novel antiretroviral therapies for human immunodeficiency virus type‐1 (HIV‐1) proteases (PRs), recent improvements in drug discovery have embraced machine learning (ML) techniques to guide the design process. This study employs ensemble learning models to identify crucial substructures as significant features for drug development. Using molecular docking techniques, a collection of 160 darunavir (DRV) analogs was designed based on these key substructures and subsequently screened using molecular docking techniques. Chemical structures with high fitness scores were selected, combined, and one‐dimensional (1D) screening based on beyond Lipinski's rule of five (bRo5) and ADME (absorption, distribution, metabolism, and excretion) prediction implemented in the Combined Analog generator Tool (CAT) program. A total of 473 screened analogs were subjected to docking analysis through convolutional neural networks scoring function against both the wild‐type (WT) and 12 major mutated PRs. DRV analogs with negative changes in binding free energy (ΔΔGbind) compared to DRV could be categorized into four attractive groups based on their interactions with the majority of vital PRs. The analysis of interaction profiles revealed that potent designed analogs, targeting both WT and mutant PRs, exhibited interactions with common key amino acid residues. This observation further confirms that the ML model‐guided approach effectively identified the substructures that play a crucial role in potent analogs. It is expected to function as a powerful computational tool, offering valuable guidance in the identification of chemical substructures for synthesis and subsequent experimental testing.