Instantaneous normal modes analysis of amorphous and supercooled silica

This is the publisher's version, also available electronically from http://scitation.aip.org/content/aip/journal/jcp/114/5/10.1063/1.1337040.The dynamics of a model for amorphous and supercooled silica(SiO2), a strong glass former, is studied using instantaneous normal mode (INM) analysis. The INM spectra at a variety of temperatures are calculated via molecular dynamics simulation. At temperatures below the glass transition temperature, the dominant contribution to the soft highly anharmonic modes comprising the imaginary frequency region of the INM spectrum are found to correspond to coupled rotations of SiO4 tetrahedral units, consistent with interpretations of neutron scattering experiments [B. B. Buchenau, H. M. Zhou, and N. Nucker, Phys. Rev. Lett. 60, 1318 (1988)] and with previous normal mode analysis of simulation results at T=0 K [S. N. Taraskin and S. R. Elliot, Phys. Rev. B 56, 8623 (1997)]

The role of localization in glasses and supercooled liquids

This is the publisher's version, also available electronically from http://scitation.aip.org/content/aip/journal/jcp/104/13/10.1063/1.471147.Localized excitations (tunneling modes, soft harmonic vibrations) are believed to play a dominant role in the thermodynamics and transport properties of glasses at low temperature. Using instantaneous normal‐mode (INM) analysis, we explore the role that such localization plays in determining the behavior of such systems in the vicinity of the glass transition. Building on our previous study [Phys. Rev. Lett. 74, 936 (1995)] we present evidence that the glass transition in two simple model systems is associated with a transition temperature below which all un‐ stable INM’s become localized. This localization transition is a possible mechanism for the change in diffusion mechanism from continuous flow to localized hopping that is believed to occur in fragile glass formers at a temperature just above T g

Molecular structural order and anomalies in liquid silica

The present investigation examines the relationship between structural order, diffusivity anomalies, and density anomalies in liquid silica by means of molecular dynamics simulations. We use previously defined orientational and translational order parameters to quantify local structural order in atomic configurations. Extensive simulations are performed at different state points to measure structural order, diffusivity, and thermodynamic properties. It is found that silica shares many trends recently reported for water [J. R. Errington and P. G. Debenedetti, Nature 409, 318 (2001)]. At intermediate densities, the distribution of local orientational order is bimodal. At fixed temperature, order parameter extrema occur upon compression: a maximum in orientational order followed by a minimum in translational order. Unlike water, however, silica's translational order parameter minimum is broad, and there is no range of thermodynamic conditions where both parameters are strictly coupled. Furthermore, the temperature-density regime where both structural order parameters decrease upon isothermal compression (the structurally anomalous regime) does not encompass the region of diffusivity anomalies, as was the case for water.Comment: 30 pages, 8 figure

The Role of Attractive Interactions in Self-Diffusion

Recently, an alternative approach to self-diffusion in atomic liquids was proposed by one of us [Vergeles, M.; Szamel, G. Chem. Phys. 1999, 110, 3009]. This approach is applicable where the concept of binary collisions breaks down and the self-diffusion coefficient is small. Predictions from this method are in quantitative agreement with molecular dynamics (MD) simulations, over a broad range of densities and temperatures, for an atomic liquid interacting with a repulsive r-12 potential. Here we extend this approach to include attractive interactions; we study a liquid interacting with the Lennard-Jones (LJ) potential. Theoretical predictions are compared to MD simulations results. To clarify the role of attractive interactions, we compare LJ results with those obtained with the repulsive part of the LJ potential. Conclusions about the role of the attractive forces in self-diffusion are discussed. I

Determination of a Focused Mini Kinase Panel for Early Identification of Selective Kinase Inhibitors

We analyzed an extensive data set of 3000 Janssen kinase inhibitors (spanning some 40 therapeutic projects) profiled at 414 kinases in the DiscoverX KINOME<i>scan</i> to better understand the necessity of using such a <i>full kinase panel</i> versus simply profiling one’s compound at a much smaller number of kinases, or <i>mini kinase panel</i> (MKP), to assess its selectivity. To this end, we generated a series of MKPs over a range of sizes and of varying kinase membership using Monte Carlo simulations. By defining the <i>kinase hit index</i> (KHI), we quantified a compound’s selectivity based on the number of kinases it hits. We find that certain combinations (rather than a random selection) of kinases can result in a much lower <i>average error</i>. Indeed, we identified a <i>focused MKP</i> with a 45.1% improvement in the average error (compared to random) that yields an overall correlation of <i>R</i>² = 0.786–0.826 for the KHI compared to the full kinase panel value. Unlike using a full kinase panel, which is both time and cost restrictive, a focused MKP is amenable to the triaging of all early stage compounds. In this way, promiscuous compounds are filtered out early on, leaving the most selective compounds for lead optimization

Determination of a Focused Mini Kinase Panel for Early Identification of Selective Kinase Inhibitors

We analyzed an extensive data set of 3000 Janssen kinase inhibitors (spanning some 40 therapeutic projects) profiled at 414 kinases in the DiscoverX KINOME<i>scan</i> to better understand the necessity of using such a <i>full kinase panel</i> versus simply profiling one’s compound at a much smaller number of kinases, or <i>mini kinase panel</i> (MKP), to assess its selectivity. To this end, we generated a series of MKPs over a range of sizes and of varying kinase membership using Monte Carlo simulations. By defining the <i>kinase hit index</i> (KHI), we quantified a compound’s selectivity based on the number of kinases it hits. We find that certain combinations (rather than a random selection) of kinases can result in a much lower <i>average error</i>. Indeed, we identified a <i>focused MKP</i> with a 45.1% improvement in the average error (compared to random) that yields an overall correlation of <i>R</i>² = 0.786–0.826 for the KHI compared to the full kinase panel value. Unlike using a full kinase panel, which is both time and cost restrictive, a focused MKP is amenable to the triaging of all early stage compounds. In this way, promiscuous compounds are filtered out early on, leaving the most selective compounds for lead optimization

D3R Grand Challenge 3: Blind Prediction of Protein-Ligand Poses and Affinity Rankings

The Drug Design Data Resource aims to test and advance the state of the art in protein-ligand modeling, by holding community-wide blinded, prediction challenges. Here, we report on our third major round, Grand Challenge 3 (GC3). Held 2017-2018, GC3 centered on the protein Cathepsin S and the kinases VEGFR2, JAK2, p38-α, TIE2, and ABL1; and included both pose- prediction and affinity-ranking components. GC3 was structured much like the prior challenges GC2015 and GC2. First, Stage 1 tested pose prediction and affinity ranking methods; then all available crystal structures were released, and Stage 2 tested only affinity rankings, now in the context of the available structures. Unique to GC3 was the addition of a Stage 1b self-docking sub-challenge, in which the protein coordinates from all of the co-crystal structures used in the cross-docking challenge were released, and participants were asked to predict the pose of CatS ligands using these newly released structures. We provide an overview of the outcomes and discuss insights into trends and best-practices.</div