Drug-likeness is a key consideration when selecting compounds during the early stages of drug discovery. However, evaluation of drug-likeness in absolute terms does not reflect adequately the whole spectrum of compound quality. More worryingly, widely used rules may inadvertently foster undesirable molecular property inflation as they permit the encroachment of rule-compliant compounds towards their boundaries. We propose a measure of drug-likeness based on the concept of desirability called the quantitative estimate of drug-likeness (QED). The empirical rationale of QED reflects the underlying distribution of molecular properties. QED is intuitive, transparent, straightforward to implement in many practical settings and allows compounds to be ranked by their relative merit. We extended the utility of QED by applying it to the problem of molecular target druggability assessment by prioritizing a large set of published bioactive compounds. The measure may also capture the abstract notion of aesthetics in medicinal chemistry.

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Quantifying the chemical beauty of drugs

Most optimization problems consist in reconciling multiple objectives with each other, particularly in food processes. For example, it is necessary to optimize different parameters such as texture, flavour, and so on, in order to formulate a new product; before using bacteria or yeasts, it is very important to find an optimal culture medium for cell growth or end product synthesis, for instance. Traditionally, objectives were either combined lo form a scalar objective, through a linear combination of multiple attributes;, or else only one was optimized and the others were turned into constraints. As these techniques depended on the user's choice, they were not adapted to solve multiple-objective problems found in the food industry, where it is more satisfactory to obtain an optimal surface in which the user will be able to choose his own working conditions. Consequently, a method that incorporates the concept of Pareto's domination would be more interesting because it would permit more general use. This w...

Multicriteria optimization using a genetic algorithm for determining a Pareto set

Inventory systems with stochastic demand and supply: Properties and approximations

Design space approach in the optimization of the spray-drying process.

In recent years, response surface methodology (RSM) has brought many attentions of many quality engineers in different industries. Most of the published literature on robust design methodology is basically concerned with optimization of a single response or quality characteristic which is often most critical to consumers. For most products, however, quality is multidimensional, so it is common to observe multiple responses in an experimental situation. Through this paper interested person will be familiarize with this methodology via surveying of the most cited technical papers. It is believed that the proposed procedure in this study can resolve a complex parameter design problem with more than two responses. It can be applied to those areas where there are large data sets and a number of responses are to be optimized simultaneously. In addition, the proposed procedure is relatively simple and can be implemented easily by using ready-made standard statistical packages. Keywords—Multi-Response Surface Methodology (MRSM), Design of Experiments (DOE), Process modeling, Quality improvement; Robust Design.

Statistical Process Optimization Through Multi-Response Surface Methodology

First introduced in 1965, the concept of a "desirability function" has gained wide acceptance as a method of determining parameter settings to optimize a process with multiple output measurements. Unfortunately, little attention has been paid to the underlying assumptions of decision theory on which the procedure is based. This paper demonstrates critical issues that can arise from blind adherence to this procedure.

"The desirability function: underlying assumptions and application implications"

The triangular density to approximate the normal density: decision rules-of-thumb

The healthcare industry currently accounts for more than 12% of the US Gross Domestic Product (GDP). The industry is struggling with a shift from the "fee for service" compensation model prevalent during the 20th Century to a delivery model based on some form of "managed care". Efforts at implementing capitation rate contracts with physicians have been significantly hampered by the inability to accurately forecast and adjust for risk differences among various practice populations. We approach the problem within the framework of state-based modelling to develop state definitions that represent homogeneous sub-populations. We use the state definitions to develop a First Order Markov Model for healthcare expense prediction and compare performance to the current state of the art expense prediction models on actual data from a family practice in Calgary.

An exploration and case study of population classification for managed healthcare within a state-based modelling framework

In order to effectively produce quality decisions in the modern credit card industry, knowledge must be gained through effective data analysis and modeling. Through the use of dynamic data-driven decision making tools and procedures, information can be gathered to successfully evaluate all aspects of credit card operations. Specifically, areas of bankruptcy, fraud, and collections were focused upon to show the salutary benefits implementation of such practices could provide. Methodologies ranging from Markov chains, to clustering, to rule-based decision theory were combined with tools such as CART, S+, Excel, and Access to yield such insights.

Douglas N. Fuller

Papers

"The desirability function: underlying assumptions and application implications"

The triangular density to approximate the normal density: decision rules-of-thumb

An exploration and case study of population classification for managed healthcare within a state-based modelling framework

Modeling and data analysis in the credit card industry: bankruptcy, fraud, and collections