L. Mark Hall

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Several QSPR models were developed for predicting intrinsic aqueous solubility, S(o). A data set of 5,964 neutral compounds was sub-divided into two classes, aromatic and non-aromatic compounds. Three models were created with different methods on both data sets: two regression models (multiple linear regression and partial least squares) and an artificial(More)
The binding affinity to human serum albumin for 94 drugs was modeled with topological descriptors of molecular structure, using as experimental data the HPLC chromatographic retention index [logk(HSA)] on immobilized albumin. The electrotopological state (E-State) along with the molecular connectivity chi indices provided the basis for a satisfactory model:(More)
The binding of beta-lactams to human serum proteins was modeled with topological descriptors of molecular structure. Experimental data was the concentration of protein-bound drug expressed as a percent of the total plasma concentration (percent fraction bound, PFB) for 87 penicillins and for 115 beta-lactams. The electrotopological state indices (E-State)(More)
A back-propagation artificial neural network (ANN) was used to create a 10-fold leave-10%-out cross-validated ensemble model of high performance liquid chromatography retention index (HPLC-RI) for a data set of 498 diverse druglike compounds. A 10-fold multiple linear regression (MLR) ensemble model of the same data was developed for comparison. Molecular(More)
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