AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared with the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user.

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AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony Ant colony optimization exploits a similar mechanism for solving optimization problems From the early nineties, when the first ant colony optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO The goal of this article is to introduce ant colony optimization and to survey its most notable applications

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Ant Colony Optimization

In this paper, we intend to formulate a new meta-heuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Levy flight behaviour of some birds and fruit flies. We validate the proposed algorithm against test functions and then compare its performance with those of genetic algorithms and particle swarm optimization. Finally, we discuss the implication of the results and suggestion for further research.

Cuckoo Search via Lévy flights

In this paper we study basic properties of so called Emergent Sorting Networks. These are directed networks which consist of router agents and buffer sites that are located between every two neighbouring agents. The agents can move objects from their input buffer sites to their output buffers. It is assumed that the objects may differ in type and are inserted randomly at an input agent. Brueckner (2000) presented a set of simple local policies for the agents which lead to a sorted outflow (batches of objects of the same type) of the objects out of an output agent. In this paper we introduce a pheromone based variant of the routing policies. The different policies are studied on quadratic and linear network topologies in terms of sorting performance and fairness.

Emergent Sorting in Networks of Router Agents

Deoxyribonucleic acid (DNA) sequencing is an important task in computational biology. In recent years the specific problem of DNA sequencing by hybridization has attracted quite a lot of interest in the optimization community. This led to the development of several metaheuristic approaches such as tabu search and evolutionary algorithms. In this work we propose an ant colony algorithm to resolve this problem. In addition, we apply our algorithm within a multi-level framework which helps in significantly reducing the computation time. The results show that our algorithm is currently among the state-of-the-art methods for this problem.

Multi-level ant colony optimization for DNA sequencing by hybridization

The edge-disjoint paths (EDP) problem The EDP problem is interesting for different research fields such as combinatorial optimization, algorithmic graph theory and operations research. In general, there is a lack of efficient algorithms for tackling the EDP problem. Only some greedy approaches and a preliminary ant colony optimization (ACO) approach (Blesa and Blum, 2004) exist for tackling the problem. The greedy approaches are used as approximation algorithms for theoretical purposes, but the quality of the solutions they obtain are susceptible to improvement. Based on the (basic) approach in Blesa and Blum (2004), we have evolved a more sophisticated ACO algorithm.

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A nature-inspired algorithm for the disjoint paths problem

Given a set of two input strings and a pattern string, the constrained longest common subsequence problem deals with finding a longest string that is a subsequence of both input strings and that contains the given pattern string as a subsequence. This problem has various applications, especially in computational biology. In this work we consider the \(\mathcal {NP}\)–hard case of the problem in which more than two input strings are given. First, we adapt an existing A\(^*\) search from two input strings to an arbitrary number m of input strings (\(m \ge 2\)). With the aim of tackling large problem instances approximately, we additionally propose a greedy heuristic and a beam search. All three algorithms are compared to an existing approximation algorithm from the literature. Beam search turns out to be the best heuristic approach, matching almost all optimal solutions obtained by A\(^*\) search for rather small instances.

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On Solving a Generalized Constrained Longest Common Subsequence Problem

The longest common palindromic subsequence (LCPS) problem requires to find a longest palindromic string that appears as subsequence in each string from a given set of input strings. The algorithms that can be found in the related literature are specific for LCPS problems with only two input strings. In contrast, in this work we consider the general case with an arbitrary number of input strings, which is NP-hard. To solve this problem we propose a fast greedy heuristic, a beam search, and an exact A\(^*\) algorithm. Moreover, A\(^*\) is extended by a simple diving mechanism as well as a combination with beam search in order to find good quality solutions already early in the search process. The most important findings that result from the experimental evaluation include that (1) A\(^*\) is able to efficiently find proven optimal solutions for smaller problem instances, (2) the anytime behavior of A\(^*\) can be significantly improved by incorporating diving or beam search, and (3) beam search is best from a purely heuristic perspective.

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Christian Blum

Papers

Emergent Sorting in Networks of Router Agents

Multi-level ant colony optimization for DNA sequencing by hybridization

A nature-inspired algorithm for the disjoint paths problem

On Solving a Generalized Constrained Longest Common Subsequence Problem

Exact and Heuristic Approaches for the Longest Common Palindromic Subsequence Problem