It is much more difficult to find on the potential energy surface (PES) a transition state (TS) than a local minimum (LM). We propose a new methodology which makes this task much easier. Applying external forces to nuclei in a molecule we can locate on PES not just one particular TS but a number of consecutive transition states. With our approach it is possible to move over PES from one transition state to another without involving any local minima. The latter can be located in a separate step through the reaction path calculations performed for every transition state found before. Preliminary results for the 2-fluorofuran molecule illustrate the usefulness of the proposed method.

The calibration of torsional interaction terms by fitting relative gas-phase conformational energies against their quantum-mechanical values is a common procedure in force-field development. However, much less attention has been paid to the optimization of third-neighbor nonbonded interaction parameters, despite their strong coupling with the torsions. This article introduces an algorithm termed LLS-SC, aimed at simultaneously parametrizing torsional and third-neighbor interaction terms based on relative conformational energies. It relies on a self-consistent (SC) procedure where each iteration involves a linear least-squares (LLS) regression followed by a geometry optimization of the reference structures. As a proof-of-principle, this method is applied to obtain torsional and third-neighbor interaction parameters for aliphatic chains in the context of the GROMOS 53A6 united-atom force field. The optimized parameter set is compared to the original one, which has been fitted manually against thermodynamic properties for small linear alkanes. The LLS-SC implementation is freely available under http://github.com/mssm-labmmol/profiler.

Density functionals at the level of the generalized gradient approximation (GGA) and a plane-wave basis set are widely used today to perform ab initio molecular dynamics (AIMD) simulations. Going up in the ladder of accuracy of density functionals from GGA (second rung) to hybrid density functionals (fourth rung) is much desired pertaining to the accuracy of the latter in describing structure, dynamics, and energetics of molecular and condensed matter systems. On the other hand, hybrid density functional based AIMD simulations are about two orders of magnitude slower than GGA based AIMD for systems containing ~100 atoms using ~100 compute cores. Two methods, namely MTACE and s-MTACE, based on a multiple time step integrator and adaptively compressed exchange operator formalism are able to provide a speed-up of about 7-9 in performing hybrid density functional based AIMD. In this work, we report an implementation of these methods using a task-group based parallelization within the CPMD program package, with the intention to take advantage of the large number of compute cores available on modern high-performance computing platforms. We present here the boost in performance achieved through this algorithm. This work also identifies the computational bottleneck in the s-MTACE method and proposes a way to overcome it.

In this study, we assess the effective inhibition of a series of thiazolidine derivatives (1a-1q) were adopting structure-based drug design. Thiazolidine is a five-membered ring structure with thioether and amino groups at positions 1 and 3. Although, thiazolidine may bind to a wide range of protein targets, it is a major heterocyclic core in medicinal chemistry. Different scoring utilities including AutoDock Vina, Glide, and MM/GBSA analysis were performed to commensurate the improvement of screening progress. The evaluated binding affinities were validated by molecular dynamics simulations over a period of 20 ns for the interactions between the Mycobacterium tuberculosis protein PrpR with three novel scaffolds (1b, 1j, and 1k). All the scaffolds exhibited distinct stable interactions with the significant residues like Arg169, Arg197, Tyr248, Arg308, and Gly311 respectively. Further, the inhibitory activities of scaffolds were predicted and evaluated by machine learning based algorithm to rank the above proposed compounds. This study reveals the potential of 1k and 1j as effective inhibitor candidates for the treatment of tuberculosis.

Using the molecular tailoring approach, a total energy scale for the push-pull effect in the range from -40 to 100 kcal/mol is created for the wide series of neutral, charged and doubly charged compounds on the chalcone platform. Taking into account similar energy scale for hydrogen bonds, the strength of the push-pull effect is ranked in the seven categories, ranging from negative (anti-push-pull) to very weak and very strong push-pull effect. It is demonstrated that the molecular properties of chalcone can be tuned prior synthesis due to the created energy scale for the push-pull effect. The single bonds of the π-spacer in the chalcones are shortened, the double ones are lengthened, and the C=O bond vibrations are red shifted when the push-pull effect is enhanced along the energy scale. The HOMO and LUMO energies change systematically while the HOMO-LUMO energy gap narrows as the strength of the push-pull effect increases.

Ab initio methods were used to determine the influence of tetrel bond formation on the acidity. The systems composed of inorganic acids and tetrafluorides of 14 group elements have been tested - HA/EF4 , where HA = H2 O, NH3 , HF, HCN, HNC, HCNO, HOCN and E = C, Si, Ge, Sn or Pb. It turns out that the electron density flow involved with formation of tetrel bond to carbon-based systems leads to negligible increase in acidity. In the case of the acceptor compounds based on the remaining 14 group elements however, the effect is much more apparent, as most of those compounds may be considered a Brønsted superacids. The electronic stability of anions formed after the deprotonation of aforementioned complexes has been investigated. Vast majority of the anions were found to exhibit significant electron binding energies.

The bonding situation of Ag(I)CO complexes having a Scorpionate ligand directly attached to the transition metal has been analyzed in detail by means of relativistic density functional theory calculations. To this end, different experimentally characterized complexes together with other representative species have been considered to rationalize the observed shift of the corresponding ν(CO) stretching frequencies and the influence of the substituents in the Scorpionate ligand. With the help of the energy decomposition analysis method combined with the natural orbital for chemical valence it is found that the main contribution to the bonding comes from the electrostatic attractions between the LAg(I) and CO fragments. Despite that, the LAg → CO π-backdonation is also significant in these species as well as in related LCu(I)CO complexes.

Simulation of crystal structures of series 1(2)-R-1(2)H-[1,2,3]triazolo[4,5-e][1,2,3,4]tetrazine 5,7-dioxides, 1,5,7-trioxides, 4,6-dioxides and 3,4,6-trioxides was carried out using an original technique based on the method of atom-atom potentials and quantum chemistry. The effect of the position of the substituent in the triazole ring on the change in the crystal structures of these compounds and their thermochemical characteristics was studied for the first time. For some of synthesized compounds, thermochemical characteristics were investigated and differential scanning calorimetry curves were obtained. Detonation parameters were calculated, on the basis of which the prospects for the use of the considered compounds were assessed.

Within substitution reactions, the Bimolecular Nucleophilic Substitution (SN 2) reaction mechanism is one of the most frequently found and studied ones. Among other factors, the easiness of the SN 2 pathway is classically considered to be determined by steric hindrance. However, the diffuse nature of the latter inevitably darkens these and other arguments holding the pillars of chemical intuition. In this work, we employ the steric energy (EST ) descriptor, formulated within the Interacting Quantum Atoms approach, to offer insights regarding this problem. The steric demands of the substrate, nucleophile and leaving group were studied using the gas-phase SN 2 reaction with different organic skeletons (CH3 , CH3 CH2 , (CH3 )2 CH, (CH3 )3 C, (CH3 )3 CCH2 ) and halogens (F, Cl, and Br) as test-bed systems. Our results show that, according to EST , the SH experienced along these simple reactions fits, in the general case, the trends predicted by a meticulous and rigorous application of chemical intuition. However, steric clash alone should not be considered as the only argument used to explain the easiness of the SN 2 reaction over different electrophiles.

Optimized link bond parameters for the Cα Cβ bond of 22 different capped amino acid model systems have been determined at SCC DFTB/mio (self-consistent charge density functional tight-binding), SCC DFTB/3ob and GFNn-xTB (n = 0, 1, and 2) level in conjunction with the AMBER 99SB, 14SB, and 19B force fields. The resulting parameter sets have been compared to newly calculated reference data obtained via resolution-of-identity 2nd order Møller-Plesset perturbation theory. The data collected in this work suggests that the optimized values in this study provide a more suitable setup of the QM/MM link bonds compared to the use of a single global setting applied to every amino acid fragmented by the QM/MM interface. The results also imply that a transfer of the ideal link bond settings between different levels of theory is not advised. In contrast, virtually identical parameters were obtained in calculations employing different variants of the AMBER force field. Considering the increasing success of tight binding based approaches being inter alia a results of their exceptional accuracy/effort ratio the provided collection of link atoms parameters provides a valuable resource for QM/MM studies of biomacromolecular systems as demonstrated in an exemplary QM/MM MD simulation of the β-amyloid/Zn2+ complex.

The influence of hydrogen bonds in model intercalated systems between guanine-cytosine and adenine-thymine DNA base pairs (bps) was analyzed with the popular intercalator 1,10-phenanthroline (phen) and derivatives obtained by substitution with OH and NH2 groups in positions 4 and 7. Semiempirical and Density Functional Theory (DFT) methods were used both including dispersion effects: PM6-DH2, M06-2X and B3LYP-D3 along with the recently developed near linear-scaling coupled cluster method DLPNO-CCSD(T) for benchmark calculations. Our results given by QTAIM and non-covalent interaction analysis confirmed the existence of hydrogen bonds created by OH and NH2 . The trends in the energy decomposition analysis for the interaction energy, ΔEint , showed that the ΔEelstat contributions are equal or even a little bit higher than the values for ΔEdisp . Such important ΔEelstat attractive contribution comes mainly from the conventional hydrogen bonds formed by OH and NH2 functional groups with DNA not only with bps but specially with the sugar and phosphate backbone. This behavior is very different from that of phen and other classical intercalators that cannot form conventional hydrogen bonds, where the ΔEdisp is the most important attractive contribution to the ΔEint . The inclusion of explicit water molecules in molecular dynamics simulations showed, as a general trend, that the hydrogen bonds with the bps disappear during the simulations but those with the sugar and phosphate backbone remain in time, which highlights the important role of the sugar and phosphate backbone in the stabilization of these systems.

Binding of dinitrogen (N2 ) to a transition metal center (M) and followed by its activation under milder conditions is no longer impossible; rather, it is routinely studied in laboratories by transition metal complexes. In contrast, binding of N2 by main group elements has been a challenge for decades, until very recently, an exotic cAAC-borylene (cAAC = cyclic alkyl(amino) carbene) species showed similar binding affinity to kinetically inert and non-polar dinitrogen (N2 ) gas under ambient conditions. Since then, N2 binding by short lived borylene species has made a captivating news in different journals for its unusual features and future prospects. Herein, we carried out different types of DFT calculations, including EDA-NOCV analysis of the relevant cAAC-boron-dinitrogen complexes and their precursors, to shed light on the deeper insight of the bonding secret (EDA-NOCV = energy decomposition analysis coupled with natural orbital for chemical valence). The hidden bonding aspects have been uncovered and are presented in details. Additionally, similar calculations have been carried out in comparison with a selected stable dinitrogen bridged-diiron(I) complex. Singlet cAAC ligand is known to be an exotic stable species which, combined with the BAr group, produces an intermediate singlet electron-deficient (cAAC)(BAr) species possessing a high lying HOMO suitable for overlapping with the high lying π*-orbital of N2 via effective π-backdonation. The BN2 interaction energy has been compared with that of the FeN2 bond. Our thorough bonding analysis might answer the unasked questions of experimental chemists about how boron compounds could mimic the transition metal of dinitrogen binding and activation, uncovering hidden bonding aspects. Importantly, Pauling repulsion energy also plays a crucial role and decides the binding efficiency in terms of intrinsic interaction energy between the boron-center and the N2 ligand.

The Poisson-Boltzmann equation offers an efficient way to study electrostatics in molecular settings. Its numerical solution with the boundary element method is widely used, as the complicated molecular surface is accurately represented by the mesh, and the point charges are accounted for explicitly. In fact, there are several well-known boundary integral formulations available in the literature. This work presents a generalized expression of the boundary integral representation of the implicit solvent model, giving rise to new forms to compute the electrostatic potential. Moreover, it proposes a strategy to build efficient preconditioners for any of the resulting systems, improving the convergence of the linear solver. We perform systematic benchmarking of a set of formulations and preconditioners, focusing on the time to solution, matrix conditioning, and eigenvalue spectrum. We see that the eigenvalue clustering is a good indicator of the matrix conditioning, and show that they can be easily manipulated by scaling the preconditioner. Our results suggest that the optimal choice is problem-size dependent, where a simpler direct formulation is the fastest for small molecules, but more involved second-kind equations are better for larger problems. We also present a fast Calderón preconditioner for first-kind formulations, which shows promising behavior for future analysis. This work sets the basis towards choosing the most convenient boundary integral formulation of the Poisson-Boltzmann equation for a given problem.

A recently proposed method based on Levitation and Blow torch effects, is employed here to see if it can separate a mixture of neopentane and n-hexane. The results show that the mixture can be separated with a hot zone temperature of just 40 K above the ambient temperature, 300 K. The two components are found to accumulate at the two extreme ends of the zeolite column. The computed separation factor is in the range of 1015 -1020 (as compared to 104 for existing separation methods). The energy expense for the separation is significantly smaller than for existing separation methods by several orders of magnitude. Transport (D11 ), self (Ds ), and distinct diffusivities (Dd ) of the mixture were computed. The contribution of distinct diffusivity to the transport diffusivity is 70% as compared to 10%-30% seen in other separation methods and is larger by 2.3 times as compared to the self-diffusivity.

Due to the presence of both a slightly acidic carbon and a slightly basic oxygen, carbon dioxide is often involved in concerted transition states (TSs) with two (or more) different molecular events interlaced in the same step. The possibility of isolating and quantitatively evaluating each molecular event would be important to characterize and understand the reaction mechanism in depth. This could be done, in principle, by measuring the relevant distances in the optimized TS, but often distances are not accurate enough, especially in the presence of many simultaneous processes. Here, we have applied the Extended Transition State-Natural Orbital for Chemical Valence-method (ETS-NOCV), also in combination with the Activation Strain Model (ASM) and Energy Decomposition Analysis (EDA), to separate and quantify these molecular events at the TS of both organometallic and organic reactions. For the former, we chose the decomposition of formic acid to CO2 by an iridium catalyst, and for the latter, a CO2 -mediated transamidation and its chemical variations (hydro- and aminolysis of an ester) as case studies. We demonstrate that the one-to-one mapping between the "molecular events" and the ETS-NOCV components is maintained along the entire lowest energy path connecting reactants and products around the TS, thus enabling a detailed picture on the relative importance of each interacting component. The methodology proposed here provides valuable insights into the effect of different chemical substituents on the reaction mechanism and promises to be generally applicable for any concerted TSs.

Multi-parameter optimization (MPO) is a major challenge in new chemical entity (NCE) drug discovery. Recently, promising results were reported for deep learning generative models applied to de novo molecular design, but, to our knowledge, until now no report was made of the value of this new technology for addressing MPO in an actual drug discovery project. In this study, we demonstrate the benefit of applying AI technology in a real drug discovery project. We evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the obtention of lead compounds meeting 11 different biological activity objectives simultaneously. Using the initial dataset of the project, we built QSAR models for all the 11 objectives, with moderate to high performance (precision between 0.67 and 1.0 on an independent test set). Our DL-based AI de novo design algorithm, combined with the QSAR models, generated 150 virtual compounds predicted as active on all objectives. Eleven were synthetized and tested. The AI-designed compounds met 9.5 objectives on average (i.e., 86% success rate) versus 6.4 (i.e., 58% success rate) for the initial molecules measured on all objectives. One of the AI-designed molecules was active on all 11 measured objectives, and two were active on 10 objectives while being in the error margin of the assay for the last one. The AI algorithm designed compounds with functional groups, which, although being rare or absent in the initial dataset, turned out to be highly beneficial for the MPO.

Drug discovery pipelines typically involve high-throughput screening of large amounts of compounds in a search of potential drugs candidates. As a chemical space of small organic molecules is huge, a "navigation" over it urges for fast and lightweight computational methods, thus promoting machine-learning approaches for processing huge pools of candidates. In this contribution, we present a graph-based deep neural network for prediction of protein-drug binding affinity and assess its predictive power under thorough testing conditions. Within the suggested approach, both protein and drug molecules are represented as graphs and passed to separate graph sub-networks, then concatenated and regressed towards a binding affinity. The neural network is trained on two binding affinity datasets-PDBbind and data imported from RCSB Protein Data Bank. In order to explore the generalization capabilities of the model we go beyond traditional random or leave-cluster-out techniques and demonstrate the need for more elaborate model performance assessment - six different strategies for test/train data partitioning (random, time- and property-arranged, protein- and ligand-clustered) with a k-fold cross-validation are engaged. Finally, we discuss the model performance in terms of a set of metrics for different split strategies and fold arrangement. Our code is available at https://github.com/SoftServeInc/affinity-by-GNN.

The generalized energy-based fragmentation (GEBF) approach is extended to facilitate ab initio investigations of structures, lattice energies, vibrational spectra and 1 H NMR chemical shifts of ionic crystals and condensed-phase ionic liquids (ILs) with the periodic boundary conditions (PBC). For selected periodic systems, our results demonstrate that the so-called PBC-GEBF approach can provide satisfactory descriptions on ground-state energies, structures, and vibrational spectra of ionic crystals and IL crystals. The PBC-GEBF approach is then applied to three realistic condensed phase systems. For three ionic crystals (LiCl, NaCl, and KCl), we apply the PBC-GEBF approach with MP2 theory as well as some popular DFT methods to investigate their crystal structures and lattice energies. Our calculations indicate that the crystal structures obtained with PBC-GEBF-MP2/6-311 + G** are very close to the corresponding X-ray structures, while PBC-GEBF-ωB97X-D/6-311 + G** provides satisfactory prediction for crystal structures and lattice energies. For two polymorphs of [n-C4 mim][Cl] crystals, we find that the PBC-GEBF approach at the M06-2X/6-311 + G** level can give a satisfactory descriptions on structures and Raman spectra of these two crystals. Furthermore, for [C2 mim][BF4 ] ILs, we demonstrate that their 1 H NMR chemical shifts can be estimated from averaging over 5 typical snapshots (extracted from MD simulations) with the PBC-GEBF approach at the B97-2/pcSseg-2 level. The calculated results account for the observed experimental data quite well. Therefore, we expect that the PBC-GEBF approach, combined with various quantum chemistry methods, will become an effective tool in predicting structures and properties of ionic crystals and condensed-phase ILs.