1,721,003 research outputs found
Optimal Transport for Free Energy Estimation
No full textOptimal transport theory is a growing field of mathematics, which has recently found many applications. Here we take advantage of optimal transport for computational free energy estimation. We show analytically, and then via simulation, that this approach is effective in terms of optimizing the barriers of an alchemical transformation
Fast and Memory-Efficient Import Vector Domain Description
No full textOne-class learning is a classical and hard computational intelligence task. In the literature, there are some effective and powerful solutions to address the problem. There are examples in the kernel machines realm, Support Vector Domain Description, and the recently proposed Import Vector Domain Description (IVDD), which directly delivers the sample probability of belonging to the class. Here, we propose and discuss two optimization techniques for IVDD to significantly improve the memory footprint and consequently to scale to datasets that are larger than the original formulation. We propose two strategies. First, we propose using random features to approximate the gaussian kernel together with a primal optimization algorithm. Second, we propose a Nyström-like approximation of the functional together with a fast converging and accurate self-consistent algorithm. In particular, we replace the a posteriori sparsity of the original optimization method of IVDD by randomly selecting a priori landmark samples in the dataset. We find this second approximation to be superior. Compared to the original IVDD with the RBF kernel, it achieves high accuracy, is much faster, and grants huge memory savings
An Ab Initio Local Principal Path Algorithm
No full textWe introduce an improved version of the principal path method, an algorithm conceived to find smooth paths between objects in space. Some key steps of the algorithm have been changed, making the solution intrinsically local and preventing it from being attracted by a global manifold. Judiciously performing the initialization step with the Dijkstra algorithm and a proper metric, the functional now only performs a final refinement of the initial solution. Hence the algorithm is stabler as the space of possible solutions has been considerably reduced with respect to the original method. We tested the proposed algorithm in 2D toy data sets (to understand the behaviour) and in high-dimensional data sets. Compared to the previous version of the algorithm, we obtained significantly stabler and more realistic generated samples
Efficient implementation of SVM training on embedded electronic systems
No full textThe implementation of training algorithms for SVMs on embedded architectures differs significantly from the electronic support of trained SVM systems. This mostly depends on the complexity and the computational intricacies brought about by the optimization process, which implies a Quadratic-Programming prob-lem and usually involves large data sets. This work presents a general approach to the efficient implementation of SVM training on Digital Signal Processor (DSP) devices. The methodology optimizes efficiency by suitably adjusting the established, effective Keerthi’s optimization algorithm for large data sets. Besides, the algorithm is reformulated to best exploit the computational features of DSP devices and boost efficiency accordingly. Experimental results tackle the training problem of SVMs by involving real-world benchmarks, and confirm both the computational efficiency of the approach
Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation
Full text linkComputational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.
Simple learning with a teacher via biased regularized least squares
No full textIn the paradigm of learning with a teacher, introduced by Vapnik, a supervised learner is trained on an augmented features space, and a student is requested to match the teacher accuracy as much as possible in a reduced feature space. In particular, in the transfer learning mode proposed by Vapnik, a method was formalized to move the knowledge from the teacher to the student. In this paper, we use biased regularized least squares as a simple yet effective method to transfer the knowledge from one learner to another, and to assess its accuracy. We achieve this by further generalizing a semi-supervised learning method, which we previously introduced. We will show that, with this approach, the teacher can be any classifier. In particular, we will employ the Relevance Vector Machine (RVM) as teacher to assess the method’s capability in transferring the knowledge in terms of classification accuracy, and in reproducing the probabilities coming from RVM. We validate the method against standard UCI datasets and systematically compare it with Vapnik’s original method in terms of accuracy and execution time. We thus demonstrate the feasibility and speed of this new approach
On the allosteric puzzle and pocket crosstalk through computational means
Full text linkAllostery is a constitutive, albeit often elusive, feature of biomolecular systems, which heavily determines their functioning. Its mechanical, entropic, long-range, ligand, and environment-dependent nature creates far from trivial interplays between residues and, in general, the secondary structure of proteins. This intricate scenario is mirrored in computational terms as different notions of "correlation" among residues and pockets can lead to different conclusions and outcomes. In this article, we put on a common ground and challenge three computational approaches for the correlation estimation task and apply them to three diverse targets of pharmaceutical interest: the androgen A2A receptor, the androgen receptor, and the EGFR kinase domain. Results show that partial results consensus can be attained, yet different notions lead to pointing the attention to different pockets and communications
Non-stationary Data Mining: the Network Security Issue
No full textData mining applications explore large amounts of heterogeneous data in search of consistent information. In such a challenging context, empirical learning methods aim to optimize prediction on unseen data, and an accurate estimate of the generalization error is of paramount importance. The paper shows that the theoretical formulation based on the Vapnik-Chervonenkis dimension (d vc ) can be of practical interest when applied to clustering methods for data-mining applications. The presented research adopts the K-Winner Machine (KWM) as a clustering-based, semi-supervised classifier; in addition to fruitful theoretical properties, the model provides a general criterion for evaluating the applicability of Vapnik's generalization predictions in data mining. The general approach is verified experimentally in the practical problem of detecting intrusions in computer networks. Empirical results prove that the KWM model can effectively support such a difficult classification task and combine unsupervised and supervised
Solubility Advantage of Amorphous Ketoprofen. Thermodynamic and Kinetic Aspects by Molecular Dynamics and Free Energy Approaches
No full textThermodynamic and kinetic aspects of crystalline (c-KTP) and amorphous (a-KTP) ketoprofen dissolution in water have been investigated by molecular dynamics simulation focusing on free energy properties. Absolute free energies of all relevant species and phases have been determined by thermodynamic integration on a novel path, first connecting the harmonic to the anharmonic system Hamiltonian at low T and then extending the result to the temperature of interest. The free energy required to transfer one ketoprofen molecule from the crystal to the solution is in fair agreement with the experimental value. The absolute free energy of the amorphous form is 19.58 kJ/mol higher than for the crystal, greatly enhancing the ketoprofen concentration in water, although as a metastable species in supersaturated solution. The kinetics of the dissolution process has been analyzed by computing the free energy profile along a reaction coordinate bringing one ketoprofen molecule from the crystal or amorphous phase to the solvated state. This computation confirms that, compared to the crystal form, the dissolution rate is nearly 7 orders of magnitude faster for the amorphous form, providing one further advantage to the latter in terms of bioavailability. The problem of drug solubility, of great practical importance, is used here as a test bed for a refined method to compute absolute free energies, which could be of great interest in biophysics and drug discovery in particular
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