Evolving Interpretable Structure−Activity Relationship Models. 2. Using Multiobjective Optimization To Derive Multiple Models

Multi-objective optimization methods in novel drug design

Introduction: In multi-objective drug design, optimization gains importance, being upgraded to a discipline that attracts its own research. Current strategies are broadly classified into single - objective optimization (SOO) and multi-objective optimization (MOO).Areas covered: Starting with SOO and the ways used to incorporate multiple criteria into it, the present review focuses on MOO techniques, their comparison, advantages, and restrictions. Pareto analysis and the concept of dominance stand in the core of MOO. The Pareto front, Pareto ranking, and limitations of Pareto-based methods, due to high dimensions and data uncertainty, are outlined. Desirability functions and the weighted sum approaches are described as stand-alone techniques to transform the MOO problem to SOO or in combination with pareto analysis and evolutionary algorithms. Representative applications in different drug research areas are also discussed.Expert opinion: Despite their limitations, the use of combined MOO techniques, as well as being complementary to SOO or in conjunction with artificial intelligence, contributes dramatically to efficient drug design, assisting decisions and increasing success probabilities. For multi-target drug design, optimization is supported by network approaches, while applicability of MOO to other fields like drug technology or biological complexity opens new perspectives in the interrelated fields of medicinal chemistry and molecular biology.

Chemoinformatics at the University of Sheffield 2002-2014

This paper summarises work in chemoinformatics carried out in the Information School of the University of Sheffield during the period 2002-2014. Research studies are described on fingerprint-based similarity searching, data fusion, applications of reduced graphs and pharmacophore mapping, and on the School's teaching in chemoinformatics.

inSARa: intuitive and interactive SAR interpretation by reduced graphs and hierarchical MCS-based network navigation

The analysis of Structure-Activity-Relationships (SAR) of small molecules is a fundamental task in drug discovery. Although a large number of methods are already published, there is still a strong need for novel intuitive approaches. The inSARa (intuitive networks for Structure-Activity Relationships analysis) method introduced herein takes advantage of the synergistic combination of reduced graphs (RG) and the intuitive maximum common substructure (MCS) concept. The main feature of the inSARa concept is a hierarchical network structure of clearly defined substructure relationships based on common pharmacophoric features. Thus, straightforward SAR interpretation is possible by interactive network navigation. When focusing on a set of active molecules at one single target, the resulting inSARa networks are shown to be valuable for various essential tasks in SAR analysis, such as the identification of activity cliffs or "activity switches", bioisosteric exchanges, common pharmacophoric features, or "SAR hotspots".

QSAR workbench: automating QSAR modeling to drive compound design

We describe the QSAR Workbench, a system for the building and analysis of QSAR models. The system is built around the Pipeline Pilot workflow tool and provides access to a variety of model building algorithms for both continuous and categorical data. Traditionally models are built on a one by one basis and fully exploring the model space of algorithms and descriptor subsets is a time consuming basis. The QSAR Workbench provides a framework to allow for multiple models to be built over a number of modeling algorithms, descriptor combinations and data splits (training and test sets). Methods to analyze and compare models are provided, enabling the user to select the most appropriate model. The Workbench provides a consistent set of routines for data preparation and chemistry normalization that are also applied for predictions. The Workbench provides a large degree of automation with the ability to publish preconfigured model building workflows for a variety of problem domains, whilst providing experienced users full access to the underlying parameterization if required. Methods are provided to allow for publication of selected models as web services, thus providing integration with the chemistry desktop. We describe the design and implementation of the QSAR Workbench and demonstrate its utility through application to two public domain datasets.

Reduced graphs and their applications in chemoinformatics

Reduced graphs provide summary representations of chemical structures by collapsing groups of connected atoms into single nodes while preserving the topology of the original structures. This chapter reviews the extensive work that has been carried out on reduced graphs at The University of Sheffield and includes discussion of their application to the representation and search of Markush structures in patents, the varied approaches that have been implemented for similarity searching, their use in cluster representation, the different ways in which they have been applied to extract structure-activity relationships and their use in encoding bioisosteres.