Biomedical subjects

Michael S Lajiness

Publications and source records attributed to Michael S Lajiness.

5 recordsLinked to original sources

PubMed2005

Hit-directed nearest-neighbor searching.

This work describes a practical strategy used at Pharmacia for identifying compounds for follow-up screening following an initial HTS campaign against targets where no 3-D structural information is available and preliminary SAR models do not exist. The approach explicitly takes into account different representations of chemistry space and identifies compounds for follow-up screening that are likely to provide the best overall coverage of the chemistry spaces considered. Specifically, the work employs hit-directed nearest-neighbor (HDNN) searching of compound databases based upon a set of "probe compounds" obtained as hits in the preliminary high-throughput screens. Four different molecular representations that generate nearly unique chemistry spaces are used. The representations include 3-D, 2-D, 2-D topological BCUTs (2-DT) and molecular fingerprints derived from substructural fragments. In the case of the BCUT representations the NN searching is distance based, while in the case of molecular fingerprints a similarity-based measure is used. Generally, the results obtained differ significantly among all four methods, that is, the sets of NN compounds have surprisingly little overlap. Moreover, in all of the four chemistry space representations, a minimum of 3- to 4-fold enrichment in actives over random screening is observed even though the actives identified in each of the sets of NNs are in large measure unique. These results suggest that use of multiple searches based upon a variety of molecular representations provides an effective way of identifying more hits in HDNN searches of chemistry spaces than can be realized with single searches.

Bacteria↗

PubMed2004

Assessment of the consistency of medicinal chemists in reviewing sets of compounds.

Medicinal chemists are frequently asked to review lists of compounds to assess their drug- or leadlike nature and to evaluate the suitability of lead compounds based on their "attractiveness" and/or synthetic feasibility as a basis for launching a drug-discovery campaign. It is often felt that one medicinal chemist's opinion is as good as any other, but is it? In an attempt to answer this question, an experiment was performed in conjunction with a recent compound acquisition program (CAP) conducted at Pharmacia. Historically, the CAP included a review of many thousands of compounds by medicinal chemists who eliminate anything deemed undesirable for any reason. In a review conducted in 2002, about 22 000 compounds requiring review by medicinal chemists were broken down into 11 lists of approximately 2000 compounds each. Unknown to the medicinal chemists, a subset of 250 compounds, previously rejected by a very experienced senior medicinal chemist, was added to each of the lists. Most of the 13 medicinal chemists who participated in this process reviewed two lists, although some only reviewed a single list and one reviewed three lists. Those compounds that were deemed unacceptable were recorded and tabulated in various ways to assess the consistency of the reviews. It was found that medicinal chemists were not very consistent in the compounds they rejected as being undesirable. The inconsistency arises from the subjective analysis that all humans utilize when considering "data sets" of any kind. This has important implications for pharmaceutical project teams where individual medicinal chemists review lists of primary screening hits to identify those compounds suitable for follow-up. Once a compound is removed from a list, it and other structurally similar compounds are effectively removed from further consideration. This can also have an impact on computational chemists who are developing models for assessing the desirability or attractiveness of different classes of compounds for lead discovery.

Chemistry, Pharmaceutical↗

PubMed2004

Strategies for the identification and generation of informative compound sets.

Mounting pressures in pharmaceutical research necessitate ever increasing efficiency to lower cost and produce results. This is especially true in the realm of high-throughput screening (HTS) where large pharmaceutical companies historically test many hundreds of thousands of compounds in the search for new drug leads. As a result of this pressure the old mantra of "screen them all" is rapidly becoming a phrase of the past and the search for new, more efficient methods for discovering leads begins. This chapter will describe some of the methods, techniques, and strategies that have been implemented at Pharmacia that attempt to identify compounds that are likely to provide the most useful information so that one might discover solid leads rapidly.

Drug Design↗

PubMed2004

Molecular properties that influence oral drug-like behavior.

Pharmaceutical companies are constantly racing to discover the next therapeutic blockbuster. The consensus in the industry is to focus on compounds that are by some measure drug-like, but in order to do this effectively a number of questions must be answered. For example, how should drug-like be defined and how might this definition be used to enhance drug discovery? Has the field moved beyond Lipinski's seminal 'rule-of-five' observations? This review offers a working definition of oral drug-likeness, describes various approaches used in its characterization and discusses its appropriate use. We will focus primarily on the use of computed molecular properties that attempt to predict the oral drug-like behavior of compounds and propose guidelines for the use of observations and trends established in existing datasets to support drug discovery efforts. In particular, this review will demonstrate how trends in simple properties of the data can be used prospectively to compare and prioritize groups of compounds, chemical libraries and different chemical series with greater reliability than for predicting drug-likeness of single compounds. It is the authors' belief, however, that properties or descriptors that will completely separate drug-like space from non-drug-like space are unlikely to be found; the focus should instead lie on overall distributions of drug-like and non-drug-like compounds in the property space that tend to overlap significantly.

Administration, Oral↗

PubMed2002

Enhancement of binary QSAR analysis by a GA-based variable selection method.

Binary quantitative structure-activity relationship (QSAR) is an approach for the analysis of high throughput screening (HTS) data by correlating structural properties of compounds with a "binary" expression of biological activity (1 = active and 0 = inactive) and calculating a probability distribution for active and inactive compounds in a training set. Successfully deriving a predictive binary or any QSAR model largely depends on the selection of a preferred set of molecular descriptors that can capture the chemico-biological interaction for a particular biological target. In this study, a genetic algorithm (GA) was applied as a variable selection method in binary QSAR analysis. This GA-based variable selection method was applied to the analysis of three diverse sets of compounds, estrogen receptor (ER) ligands, carbonic anhydrase II inhibitors, and monoamine oxidase (MAO) inhibitors. Out of a variable pool of 150 molecular descriptors, predictive binary QSAR models were obtained for all three sets of compounds within a reasonable number of GA generations. The results indicate that the GA is a very effective variable selection approach for binary QSAR analysis.

Algorithms↗