397 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Computational Approaches to Drug Profiling and Drug-Protein Interactions
Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a
long period of stagnation in drug approvals. Due to the extreme costs associated with
introducing a drug to the market, locating and understanding the reasons for clinical failure
is key to future productivity. As part of this PhD, three main contributions were made in
this respect. First, the web platform, LigNFam enables users to interactively explore
similarity relationships between âdrug likeâ molecules and the proteins they bind. Secondly,
two deep-learning-based binding site comparison tools were developed, competing with
the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the
open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold
relationships and has already been used in multiple projects, including integration into a
virtual screening pipeline to increase the tractability of ultra-large screening experiments.
Together, and with existing tools, the contributions made will aid in the understanding of
drug-protein relationships, particularly in the fields of off-target prediction and drug
repurposing, helping to design better drugs faster
Machine Learning Small Molecule Properties in Drug Discovery
Machine learning (ML) is a promising approach for predicting small molecule
properties in drug discovery. Here, we provide a comprehensive overview of
various ML methods introduced for this purpose in recent years. We review a
wide range of properties, including binding affinities, solubility, and ADMET
(Absorption, Distribution, Metabolism, Excretion, and Toxicity). We discuss
existing popular datasets and molecular descriptors and embeddings, such as
chemical fingerprints and graph-based neural networks. We highlight also
challenges of predicting and optimizing multiple properties during hit-to-lead
and lead optimization stages of drug discovery and explore briefly possible
multi-objective optimization techniques that can be used to balance diverse
properties while optimizing lead candidates. Finally, techniques to provide an
understanding of model predictions, especially for critical decision-making in
drug discovery are assessed. Overall, this review provides insights into the
landscape of ML models for small molecule property predictions in drug
discovery. So far, there are multiple diverse approaches, but their
performances are often comparable. Neural networks, while more flexible, do not
always outperform simpler models. This shows that the availability of
high-quality training data remains crucial for training accurate models and
there is a need for standardized benchmarks, additional performance metrics,
and best practices to enable richer comparisons between the different
techniques and models that can shed a better light on the differences between
the many techniques.Comment: 46 pages, 1 figur
Analysing trajectory similarity and improving graph dilation
In this thesis, we focus on two topics in computational geometry. The first topic is analysing trajectory similarity. A trajectory tracks the movement of an object over time. A common way to analyse trajectories is by finding similarities. The Fr\'echet distance is a similarity measure that has gained popularity in the theory community, since it takes the continuity of the curves into account. One way to analyse trajectories using the Fr\'echet distance is to cluster trajectories into groups of similar trajectories. For vehicle trajectories, another way to analyse trajectories is to compute the path on the underlying road network that best represents the trajectory. The second topic is improving graph dilation. Dilation measures the quality of a network in applications such as transportation and communication networks. Spanners are low dilation graphs with not too many edges. Most of the literature on spanners focuses on building the graph from scratch. We instead focus on adding edges to improve the dilation of an existing graph
Security and Privacy for Modern Wireless Communication Systems
The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in nodeâedgeâcloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Spherical and Hyperbolic Toric Topology-Based Codes On Graph Embedding for Ising MRF Models: Classical and Quantum Topology Machine Learning
The paper introduces the application of information geometry to describe the
ground states of Ising models by utilizing parity-check matrices of cyclic and
quasi-cyclic codes on toric and spherical topologies. The approach establishes
a connection between machine learning and error-correcting coding. This
proposed approach has implications for the development of new embedding methods
based on trapping sets. Statistical physics and number geometry applied for
optimize error-correcting codes, leading to these embedding and sparse
factorization methods. The paper establishes a direct connection between DNN
architecture and error-correcting coding by demonstrating how state-of-the-art
architectures (ChordMixer, Mega, Mega-chunk, CDIL, ...) from the long-range
arena can be equivalent to of block and convolutional LDPC codes (Cage-graph,
Repeat Accumulate). QC codes correspond to certain types of chemical elements,
with the carbon element being represented by the mixed automorphism
Shu-Lin-Fossorier QC-LDPC code. The connections between Belief Propagation and
the Permanent, Bethe-Permanent, Nishimori Temperature, and Bethe-Hessian Matrix
are elaborated upon in detail. The Quantum Approximate Optimization Algorithm
(QAOA) used in the Sherrington-Kirkpatrick Ising model can be seen as analogous
to the back-propagation loss function landscape in training DNNs. This
similarity creates a comparable problem with TS pseudo-codeword, resembling the
belief propagation method. Additionally, the layer depth in QAOA correlates to
the number of decoding belief propagation iterations in the Wiberg decoding
tree. Overall, this work has the potential to advance multiple fields, from
Information Theory, DNN architecture design (sparse and structured prior graph
topology), efficient hardware design for Quantum and Classical DPU/TPU (graph,
quantize and shift register architect.) to Materials Science and beyond.Comment: 71 pages, 42 Figures, 1 Table, 1 Appendix. arXiv admin note: text
overlap with arXiv:2109.08184 by other author
Network polarization, filter bubbles, and echo chambers: An annotated review of measures and reduction methods
Polarization arises when the underlying network connecting the members of a
community or society becomes characterized by highly connected groups with weak
inter-group connectivity. The increasing polarization, the strengthening of
echo chambers, and the isolation caused by information filters in social
networks are increasingly attracting the attention of researchers from
different areas of knowledge such as computer science, economics, social and
political sciences. This work presents an annotated review of network
polarization measures and models used to handle the polarization. Several
approaches for measuring polarization in graphs and networks were identified,
including those based on homophily, modularity, random walks, and balance
theory. The strategies used for reducing polarization include methods that
propose edge or node editions (including insertions or deletions, as well as
edge weight modifications), changes in social network design, or changes in the
recommendation systems embedded in these networks.Comment: Corrected a typo in Section 3.2; the rest remains unchange
Applications of Molecular Dynamics simulations for biomolecular systems and improvements to density-based clustering in the analysis
Molecular Dynamics simulations provide a powerful tool to study biomolecular systems with atomistic detail. The key to better understand the function and behaviour of these molecules can often be found in their structural variability. Simulations can help to expose this information that is otherwise experimentally hard or impossible to attain. This work covers two application examples for which a sampling and a characterisation of the conformational ensemble could reveal the structural basis to answer a topical research question. For the fungal toxin phalloidinâa small bicyclic peptideâobserved product ratios in different cyclisation reactions could be rationalised by assessing the conformational pre-organisation of precursor fragments. For the C-type lectin receptor langerin, conformational changes induced by different side-chain protonations could deliver an explanation
of the pH-dependency in the proteinâs calcium-binding. The investigations were accompanied by the continued development of a density-based clustering protocol into a respective software package, which is generally well applicable for the use case of extracting conformational states from Molecular Dynamics data
Optimization and coarse-grid selection for algebraic multigrid
Multigrid methods are often the most efficient approaches for solving the very
large linear systems that arise from discretized PDEs and other problems. Algebraic
multigrid (AMG) methods are used when the discretization lacks the structure needed
to enable more efficient geometric multigrid techniques. AMG methods rely in part
on heuristic graph algorithms to achieve their performance. Reduction-based AMG
(AMGr) algorithms attempt to formalize these heuristics.
The main focus of this thesis is to develop eâ”ective algebraic multigrid methods.
A key step in all AMG approaches is the choice of the coarse/fine partitioning, aiming
to balance the convergence of the iteration with its cost. In past work (MacLachlan
and Saad, A greedy strategy for coarse-grid selection, SISC 2007), a constrained
combinatorial optimization problem was used to define the âbestâ coarse grid within
the setting of two-level reduction-based AMG and was shown to be NP-complete. In
the first part of the thesis, a new coarsening algorithm based on simulated annealing
has been developed to solve this problem. The new coarsening algorithm gives better
results than the greedy algorithm developed previously.
The goal of the second part of the thesis is to improve the classical AMGr method.
Convergence factor bounds do not hold when AMGr algorithms are applied to matrices
that are not diagonally dominant. In this part of our research, we present
modifications to the classical AMGr algorithm that improve its performance on such
matrices. For non-diagonally dominant matrices, we find that strength of connection
plays a vital role in the performance of AMGr. To generalize the diagonal
approximations of AFF used in classical AMGr, we use a sparse approximate inverse
(SPAI) method, with nonzero pattern determined by strong connections, to define
the AMGr-style interpolation operator, coupled with rescaling based on relaxed vectors.
We present numerical results demonstrating the robustness of this approach for
non-diagonally dominant systems.
In the third part of this research, we have developed an improved deterministic
coarsening algorithm that generalizes an existing technique known as Lloydâs algorithm.
The improved algorithm provides better control of the number of clusters than
classical approaches and attempts to provide more âcompactâ groupings
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