Biomedical subjects

Donald P Visco

Publications and source records attributed to Donald P Visco.

6 recordsLinked to original sources

PubMed2006

Isothermal compressibility maxima of hydrogen fluoride in the supercritical and superheated vapor regions.

The highly nonideal behavior of hydrogen fluoride (HF) vapor has been considered to be the origin of its numerous vapor phase anomalies. In this work, we report one such potential vapor phase anomaly for HF. For a nonassociating substance like propane, the response functions go through a maximum only once in the supercritical region. However, for HF, when an association model is used to predict the isothermal compressibility (KT), it exhibits a maximum in the supercritical region more than once, and this peak extends well in to the superheated vapor region upon decompression. This theoretical prediction is also supported by two other models recently developed for HF. Note that experimental values of KT for HF have not been reported in the literature so far. Preliminary investigations on this KT maximum for HF have suggested no reentrant spinodal, singularity-free scenario, or any additional first-order phase transition, unlike water, and, also, no lambda (or higher-order phase) transitions, unlike liquid helium. However, this KT peak is similar to the experimentally supported heat capacity (CP) peak of HF which extends into the supercritical and superheated vapor regions. Similar to the CP peak, which is understood based on vapor-phase clustering in HF, we relate KT to the derivatives of enthalpy and entropy of the system. Also, we analyze some of the P-v-T experimental data that are available to provide an overview of the KT behavior in the region of interest, and compare them with the model results. Finally, to explore the effect of including a distribution pattern for the oligomers, we report the results on a model that only includes association. Using this approach, we report KT results with and without a Poisson-type oligomer distribution and show that the KT appears once this distribution scheme is specified.

Journal Article↗

PubMed2004

Impulse penetration into idealized granular beds: behavior of cumulative surface kinetic energy.

We report a particle dynamics based simulational study of the propagation of delta function mechanical impulses in idealized three-dimensional hexagonal close packed lattices of monosized Hertz spheres. This paper presents five key results on the kinetic energy of grains at the surface of a granular bed after the generation of a normal impulse into the bed. (i) We find that the time integrated or cumulative average kinetic energy per surface grain, kappa, drops as an impulse penetrates into the bed. The minimum value of kappa, say kappa(0), is reached at some time t=tau after the impulse has been generated. (ii) This value, kappa(0), depends upon the restitutional losses at the grain contacts and kappa(0) increases as restitutional losses at granular contacts increase in magnitude. (iii) The asymptotic value of kappa is denoted by kappa(final) . Our data show that increasing the area across which an impulse is generated, A, leads to kappa(final) proportional to A(-1/2) . (iv) If we assign random masses to our monosized grains, kappa(final) grows quadratically as a function of the range of mass variation about a mean mass. We find that at large times, i.e., t>>tau , kappa proportional to (1-exp [k (1-t/tau)]) , where the constant k is roughly independent of restitution for the typical values of restitution encountered. (v) Our data suggest that at early times, the backscattering process carries signatures of ballistic propagation of the mechanical energy while at late times, the backscattering process is reminiscent of vibrations of an essentially ergodic system. Given the ballisticlike propagation of mechanical energy into granular beds, we conclude that a wave equation based description of mechanical energy propagation into granular beds may not always be appropriate.

Journal Article↗

PubMed2004

The signature molecular descriptor. 3. Inverse-quantitative structure-activity relationship of ICAM-1 inhibitory peptides.

We present a methodology for solving the inverse-quantitative structure-activity relationship (QSAR) problem using the molecular descriptor called signature. This methodology is detailed in four parts. First, we create a QSAR equation that correlates the occurrence of a signature to the activity values using a stepwise multilinear regression technique. Second, we construct constraint equations, specifically the graphicality and consistency equations, which facilitate the reconstruction of the solution compounds directly from the signatures. Third, we solve the set of constraint equations, which are both linear and Diophantine in nature. Last, we reconstruct and enumerate the solution molecules and calculate their activity values from the QSAR equation. We apply this inverse-QSAR method to a small set of LFA-1/ICAM-1 peptide inhibitors to assist in the search and design of more-potent inhibitory compounds. Many novel inhibitors were predicted, a number of which are predicted to be more potent than the strongest inhibitor in the training set. Two of the more potent inhibitors were synthesized and tested in-vivo, confirming them to be the strongest inhibiting peptides to date. Some of these compounds can be recycled to train a new QSAR and develop a more focused library of lead compounds.

Drug Design↗

PubMed2002

Developing a methodology for an inverse quantitative structure-activity relationship using the signature molecular descriptor.

The concept of signature as a molecular descriptor is introduced and various topological indices used in quantitative structure-activity relationships (QSARs) are expressed as functions of the new descriptor. The effectiveness of signature versus commonly used descriptors in QSAR analysis is demonstrated by correlating the activities of 121 HIV-1 protease inhibitors. Our approach to the inverse-QSAR problem consists of first finding the optimum sets of descriptor values best matching a target activity and then generating a focused library of candidate structures from the solution set of descriptor values. Both steps are facilitated by the use of signature.

Algorithms↗

PubMedDate not supplied

The signature molecular descriptor. 1. Using extended valence sequences in QSAR and QSPR studies.

We present a new descriptor named signature based on extended valence sequence. The signature of an atom is a canonical representation of the atom's environment up to a predefined height h. The signature of a molecule is a vector of occurrence numbers of atomic signatures. Two QSAR and QSPR models based on signature are compared with models obtained using popular molecular 2D descriptors taken from a commercially available software (Molconn-Z). One set contains the inhibition concentration at 50% for 121 HIV-1 protease inhibitors, while the second set contains 12865 octanol/water partitioning coefficients (Log P). For both data sets, the models created by signature performed comparable to those from the commercially available descriptors in both correlating the data and in predicting test set values not used in the parametrization. While probing signature's QSAR and QSPR performances, we demonstrates that for any given molecule of diameter D, there is a molecular signature of height h </= D+1, from which any 2D descriptor can be computed. As a consequence of this finding any QSAR or QSPR involving 2D descriptors can be replaced with a relationship involving occurrence number of atomic signatures.

Algorithms↗

PubMedDate not supplied

The signature molecular descriptor. 2. Enumerating molecules from their extended valence sequences.

We present a new algorithm that enumerates molecular structures matching a predefined extended valence sequence or signature. The algorithm can construct molecular structures composed of about 50 non-hydrogen atoms in CPU seconds time scale. The algorithm is run to produce all molecular structures matching the binding affinities (IC(50)) of some HIV-1 protease inhibitors. The algorithm is also used to compute the degeneracy, or the number of molecular structures, corresponding to a given signature. Signature degeneracy is systematically studied for varying signature heights on four molecular series, alkanes, alcohols, fullerene-type structures, and peptides. Signature degeneracy is compared with similar results obtained with popular topological indices (TIs). As a general rule, we find that signature degeneracy decreases as the signature height increases. We also find that alkanes, alcohols, and fullerene-type structures comprising n non-hydrogen atoms are uniquely characterized by signatures of height n/4, while peptides up to 4000 amino acids can be singled out with signatures of heights as small as 2 and 3.

Algorithms↗