Artificial intelligence in multi-objective drug design.

The factors determining a drug's success are manifold, making de novo drug design an inherently multi-objective optimisation (MOO) problem. With the advent of machine learning and optimisation methods, the field of multi-objective compound design has seen a rapid increase in developments and applications. Population-based metaheuris-tics and deep reinforcement learning are the most commonly used artificial intelligence methods in the field, but recently conditional learning methods are gaining popularity.

DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology.

In polypharmacology drugs are required to bind to multiple specific targets, for example to enhance efficacy or to reduce resistance formation. Although deep learning has achieved a breakthrough in de novo design in drug discovery, most of its applications only focus on a single drug target to generate drug-like active molecules. However, in reality drug molecules often interact with more than one target which can have desired (polypharmacology) or undesired (toxicity) effects.

A multiple classifier system identifies novel cannabinoid CB2 receptor ligands.

Drugs have become an essential part of our lives due to their ability to improve people's health and quality of life. However, for many diseases, approved drugs are not yet available or existing drugs have undesirable side effects, making the pharmaceutical industry strive to discover new drugs and active compounds. The development of drugs is an expensive process, which typically starts with the detection of candidate molecules (screening) after a protein target has been identified.

Mirrored Orthogonal Sampling for Covariance Matrix Adaptation Evolution Strategies.

Generating more evenly distributed samples in high dimensional search spaces is the major purpose of the recently proposed technique for evolution strategies. The diversity of the mutation samples is enlarged and the convergence rate is therefore improved by the mirrored sampling. Motivated by the mirrored sampling technique, this article introduces a new derandomized sampling technique called .

The Set-Based Hypervolume Newton Method for Bi-Objective Optimization.

In this paper, we propagate the use of a set-based Newton method that enables computing a finite size approximation of the Pareto front (PF) of a given twice continuously differentiable bi-objective optimization problem (BOP). To this end, we first derive analytically the Hessian matrix of the hypervolume indicator, a widely used performance indicator for PF approximation sets. Based on this, we propose the hypervolume Newton method (HNM) for hypervolume maximization of a given set of candidate solutions.

A tutorial on multiobjective optimization: fundamentals and evolutionary methods.

In almost no other field of computer science, the idea of using bio-inspired search paradigms has been so useful as in solving multiobjective optimization problems. The idea of using a population of search agents that collectively approximate the Pareto front resonates well with processes in natural evolution, immune systems, and swarm intelligence. Methods such as NSGA-II, SPEA2, SMS-EMOA, MOPSO, and MOEA/D became standard solvers when it comes to solving multiobjective optimization problems.

Dynamic vehicle routing with time windows in theory and practice.

The vehicle routing problem is a classical combinatorial optimization problem. This work is about a variant of the vehicle routing problem with dynamically changing orders and time windows. In real-world applications often the demands change during operation time.

Major aging-associated RNA expressions change at two distinct age-positions.

Background: Genome-wide expression profiles are altered during biological aging and can describe molecular regulation of tissue degeneration. Age-regulated mRNA expression trends from cross-sectional studies could describe how aging progresses. We developed a novel statistical methodology to identify age-regulated expression trends in cross-sectional datasets.

Multi-objective evolutionary design of adenosine receptor ligands.

A novel multiobjective evolutionary algorithm (MOEA) for de novo design was developed and applied to the discovery of new adenosine receptor antagonists. This method consists of several iterative cycles of structure generation, evaluation, and selection. We applied an evolutionary algorithm (the so-called Molecule Commander) to generate candidate A1 adenosine receptor antagonists, which were evaluated against multiple criteria and objectives consisting of high (predicted) affinity and selectivity for the receptor, together with good ADMET properties.

Mixed integer evolution strategies for parameter optimization.

Evolution strategies (ESs) are powerful probabilistic search and optimization algorithms gleaned from biological evolution theory. They have been successfully applied to a wide range of real world applications. The modern ESs are mainly designed for solving continuous parameter optimization problems.

A novel chemogenomics analysis of G protein-coupled receptors (GPCRs) and their ligands: a potential strategy for receptor de-orphanization.

Background: G protein-coupled receptors (GPCRs) represent a family of well-characterized drug targets with significant therapeutic value. Phylogenetic classifications may help to understand the characteristics of individual GPCRs and their subtypes. Previous phylogenetic classifications were all based on the sequences of receptors, adding only minor information about the ligand binding properties of the receptors.

Adaptive niche radii and niche shapes approaches for niching with the CMA-ES.

While the motivation and usefulness of niching methods is beyond doubt, the relaxation of assumptions and limitations concerning the hypothetical search landscape is much needed if niching is to be valid in a broader range of applications. Upon the introduction of radii-based niching methods with derandomized evolution strategies (ES), the purpose of this study is to address the so-called niche radius problem. A new concept of an adaptive individual niche radius is applied to niching with the covariance matrix adaptation evolution strategy (CMA-ES).

Shift work, chronodisruption and cancer?--The IARC 2007 challenge for research and prevention and 10 theses from the Cologne Colloquium 2008.

In October of 2007, an IARC panel of 24 scientists systematically evaluated epidemiologic, experimental, and mechanistic data and concluded that shift work that involves circadian or chronodisruption is probably carcinogenic in humans. In view of the possible scope of the problem--shift work is widespread and unavoidable on one hand and breast cancer and prostate cancer, which may be causally associated with chronodisruption, are epidemic worldwide on the other--German representatives of science and occupational medicine discussed the experimental and epidemiologic background and possible implications of the challenge identified by the International Agency for Research on Cancer (IARC) at a colloquium in Cologne in September 2008. This overview summarizes the key ideas presented at the Cologne Colloquium and offers 10 theses concerning the need for targeted studies and the necessity to develop possible means of prevention.