LiHong Hu

Identification of influenza A nucleoprotein as an antiviral target

Influenza A remains a significant public health challenge because of the emergence of antigenically shifted or highly virulent strains. Antiviral resistance to available drugs such as adamantanes or neuraminidase inhibitors has appeared rapidly, creating a need for new antiviral targets and new drugs for influenza virus infections. Using forward chemical genetics, we have identified influenza A nucleoprotein (NP) as a druggable target and found a small-molecule compound, nucleozin, that triggers the aggregation of NP and inhibits its nuclear accumulation. Nucleozin impeded influenza A virus replication in vitro with a nanomolar median effective concentration (EC50) and protected mice challenged with lethal doses of avian influenza A H5N1. Our results demonstrate that viral NP is a valid target for the development of small-molecule therapies.

Identification of novel small-molecule inhibitors of severe acute respiratory syndrome-associated coronavirus by chemical genetics.

Abstract The severe acute respiratory syndrome-associated coronavirus (SARS-CoV) infected more than 8,000 people across 29 countries and caused more than 900 fatalities. Based on the concept of chemical genetics, we screened 50,240 structurally diverse small molecules from which we identified 104 compounds with anti-SARS-CoV activity. Of these 104 compounds, 2 target the SARS-CoV main protease (Mpro), 7 target helicase (Hel), and 18 target spike (S) protein-angiotensin-converting enzyme 2 (ACE2)-mediated viral entry. The EC50 of the majority of the 104 compounds determined by SARS-CoV plaque reduction assay were found to be at low micromolar range. Three selected compounds, MP576, HE602, and VE607, validated to be inhibitors of SARS-CoV Mpro, Hel, and viral entry, respectively, exhibited potent antiviral activity (EC50 < 10 μM) and comparable inhibitory activities in target-specific in vitro assays.

Combined first-principles calculation and neural-network correction approach for heat of formation

Despite their success, the results of first-principles quantum mechanical calculations contain inherent numerical errors caused by various intrinsic approximations. We propose here a neural-network-based algorithm to greatly reduce these inherent errors. As a demonstration, this combined quantum mechanical calculation and neural-network correction approach is applied to the evaluation of standard heat of formation ΔfH⊖ for 180 small- to medium-sized organic molecules at 298 K. A dramatic reduction of numerical errors is clearly shown with systematic deviation being eliminated. For example, the root-mean-square deviation of the calculated ΔfH⊖ for the 180 molecules is reduced from 21.4 to 3.1 kcal mol−1 for B3LYP/6-311+G(d,p) and from 12.0 to 3.3 kcal mol−1 for B3LYP/6-311+G(3df,2p) before and after the neural-network correction.

Improving the accuracy of density-functional theory calculation: The genetic algorithm and neural network approach

The combination of genetic algorithm and neural network approach (GANN) has been developed to improve the calculation accuracy of density functional theory. As a demonstration, this combined quantum mechanical calculation and GANN correction approach has been applied to evaluate the optical absorption energies of 150 organic molecules. The neural network approach reduces the root-mean-square (rms) deviation of the calculated absorption energies of 150 organic molecules from 0.47 to 0.22 eV for the TDDFTB3LYP6-31G(d) calculation, and the newly developed GANN correction approach reduces the rms deviation to 0.16 eV.

A Combined First-principles Calculation and Neural Networks Correction Approach for Evaluating Gibbs Energy of Formation

Despite of their successes, the results of first-principles quantum mechanical calculations contain inherent numerical errors that are caused by inadequate treatment of electron correlation, incompleteness of basis sets, relativistic effects or approximated exchange-correlation functionals. In this work, we develop a combined density-functional theory and neural-network correction (DFT-NEURON) approach to reduce drastically these errors, and apply the resulting approach to determine the standard Gibbs energy of formation ΔG 0 at 298 K for small- and medium-sized organic molecules. The root mean square deviation of the calculated ΔG 0 for 180 molecules is reduced from 22.3 kcal · mol-1 to 3.0 kcal · mol-1 for B3LYP/6-311+G(d,p). We examine further the selection of physical descriptors for the neural network.

Time-dependent density functional theory based Ehrenfest dynamics

Time-dependent density functional theory based Ehrenfest dynamics with atom-centered basis functions is developed in present work. The equation of motion for electrons is formulated in terms of first-order reduced density matrix and an additional term arises due to the time-dependence of basis functions through their dependence on nuclear coordinates. This time-dependence of basis functions together with the imaginary part of density matrix leads to an additional term for nuclear force. The effects of the two additional terms are examined by studying the dynamics of H(2) and C(2)H(4), and it is concluded that the inclusion of these two terms is essential for correct electronic and nuclear dynamics.

Polymeric architectures of bismuth citrate based on dimeric building blocks

Four bismuth complexes, (H2En)[Bi2(cit)2(H2O)4/3]·(H2O)x (1), (H2En)3[Bi2(cit)2Cl4]·(H2O)x (2), (HPy)2[Bi2(cit)2(H2O)8/5]·(H2O)x (3) and (H2En)[Bi2(cit)2](H2O)x (4) [cit = citrate4−; En = ethylenediamine; Py = pyridine] have been synthesized and crystallized. The crystal structures reveal that the basic building blocks in all of these complexes are bismuth citrate dimeric units which combine to form polymeric architectures. The embedded protonated ethylenediamine and pyridine moieties in the polymeric frameworks have been identified by X-ray crystallography and solid-state cross polarization/magic angle spinning (CP/MAS) 13C NMR. Based on the framework of complex 1, a structural model of a clinically used antiulcer drug, ranitidine bismuth citrate (RBC) was generated. The behavior of the protonated amine-bismuth citrate complexes in acidic aqueous solution has been studied by electrospray ionization-mass spectrometry (ESI-MS).

Improving the Performance of Long-Range-Corrected Exchange-Correlation Functional with an Embedded Neural Network

A machine-learning-based exchange-correlation functional is proposed for general-purpose density functional theory calculations. It is built upon the long-range-corrected Becke-Lee-Yang-Parr (LC-BLYP) functional, along with an embedded neural network which determines the value of the range-separation parameter μ for every individual system. The structure and the weights of the neural network are optimized with a reference data set containing 368 highly accurate thermochemical and kinetic energies. The newly developed functional (LC-BLYP-NN) achieves a balanced performance for a variety of energetic properties investigated. It largely improves the accuracy of atomization energies and heats of formation on which the original LC-BLYP with a fixed μ performs rather poorly. Meanwhile, it yields a similar or slightly compromised accuracy for ionization potentials, electron affinities, and reaction barriers, for which the original LC-BLYP works reasonably well. This work clearly highlights the potential usefulness of machine-learning techniques for improving density functional calculations.