Tree Decomposition Research Articles

SAT-Based Tree Decomposition with Iterative Cascading Policy Selection

Solvers for propositional satisfiability (SAT) effectively tackle hard optimization problems. However, translating to SAT can cause a significant size increase, restricting its use to smaller instances. To mitigate this, frameworks using multiple local SAT calls for gradually improving a heuristic solution have been proposed. The performance of such algorithmic frameworks heavily relies on critical parameters, including the size of selected local instances and the time allocated per SAT call. This paper examines the automated configuration of the treewidth SAT-based local improvement method (TW-SLIM) framework, which uses multiple SAT calls for computing tree decompositions of small width, a fundamental problem in combinatorial optimization. We explore various TW-SLIM configuration methods, including offline learning and real-time adjustments, significantly outperforming default settings in multi-SAT scenarios with changing problems. Building upon insights gained from offline training and real-time configurations for TW-SLIM, we propose the iterative cascading policy—a novel hybrid technique that uniquely combines both. The iterative cascading policy employs a pool of 30 configurations obtained through clustering-based offline methods, deploying them in dynamic cascades across multiple rounds. In each round, the 30 configurations are tested according to the cascading ordering, and the best tree decomposition is retained for further improvement, with the option to adjust the following ordering of cascades. This iterative approach significantly enhances the performance of TW-SLIM beyond baseline results, even within varying global timeouts. This highlights the effectiveness of the proposed iterative cascading policy in enhancing the efficiency and efficacy of complex algorithmic frameworks like TW-SLIM.

Infrared Small Target Detection Based on Tensor Tree Decomposition and Self-Adaptive Local Prior

Infrared small target detection plays a crucial role in both military and civilian systems. However, current detection methods face significant challenges in complex scenes, such as inaccurate background estimation, inability to distinguish targets from similar non-target points, and poor robustness across various scenes. To address these issues, this study presents a novel spatial–temporal tensor model for infrared small target detection. In our method, we introduce the tensor tree rank to capture global structure in a more balanced strategy, which helps achieve more accurate background estimation. Meanwhile, we design a novel self-adaptive local prior weight by evaluating the level of clutter and noise content in the image. It mitigates the imbalance between target enhancement and background suppression. Then, the spatial–temporal total variation (STTV) is used as a joint regularization term to help better remove noise and obtain better detection performance. Finally, the proposed model is efficiently solved by the alternating direction multiplier method (ADMM). Extensive experiments demonstrate that our method achieves superior detection performance when compared with other state-of-the-art methods in terms of target enhancement, background suppression, and robustness across various complex scenes. Furthermore, we conduct an ablation study to validate the effectiveness of each module in the proposed model.

Infrared: a declarative tree decomposition-powered framework for bioinformatics.

Many bioinformatics problems can be approached as optimization or controlled sampling tasks, and solved exactly and efficiently using Dynamic Programming (DP). However, such exact methods are typically tailored towards specific settings, complex to develop, and hard to implement and adapt to problem variations. We introduce the Infrared framework to overcome such hindrances for a large class of problems. Its underlying paradigm is tailored toward problems that can be declaratively formalized as sparse feature networks, a generalization of constraint networks. Classic Boolean constraints specify a search space, consisting of putative solutions whose evaluation is performed through a combination of features. Problems are then solved using generic cluster tree elimination algorithms over a tree decomposition of the feature network. Their overall complexities are linear on the number of variables, and only exponential in the treewidth of the feature network. For sparse feature networks, associated with low to moderate treewidths, these algorithms allow to find optimal solutions, or generate controlled samples, with practical empirical efficiency. Implementing these methods, the Infrared software allows Python programmers to rapidly develop exact optimization and sampling applications based on a tree decomposition-based efficient processing. Instead of directly coding specialized algorithms, problems are declaratively modeled as sets of variables over finite domains, whose dependencies are captured by constraints and functions. Such models are then automatically solved by generic DP algorithms. To illustrate the applicability of Infrared in bioinformatics and guide new users, we model and discuss variants of bioinformatics applications. We provide reimplementations and extensions of methods for RNA design, RNA sequence-structure alignment, parsimony-driven inference of ancestral traits in phylogenetic trees/networks, and design of coding sequences. Moreover, we demonstrate multidimensional Boltzmann sampling. These applications of the framework-together with our novel results-underline the practical relevance of Infrared. Remarkably, the achieved complexities are typically equivalent to the ones of specialized algorithms and implementations. Infrared is available at https://amibio.gitlabpages.inria.fr/Infrared with extensive documentation, including various usage examples and API reference; it can be installed using Conda or from source.

Domination of triangulated discs and maximal outerplanar graphs

Given an infinite family of graphs, its domination ratio is the smallest real k such that for every large enough graph in the family, the ratio between its domination number and order is at most k. We give bounds for the domination ratios of various families of triangulated discs, disproving two conjectures of Tokunaga. For instance, we show that the domination ratio of triangulated discs of minimum degree 3 is at least 3/10. We also give a natural extension of a theorem on the domination number of maximal outerplane graphs, proven by Campos and Wakabayashi, and independently Tokunaga. Motivated by the Matheson-Tarjan Conjecture, we present observations on dominating triangulations via Jackson-Yu decomposition trees as well as an approach on how to efficiently dominate, given a triangulation, many large subtriangulations thereof. Throughout the paper, results are accompanied by computational experiments.

Emerging Microplastics Alter the Influences of Soil Animals on the Fungal Community Structure in Determining the Litter Decomposition of a Deciduous Tree

Microplastics enter forest ecosystems in a variety of ways, including through atmospheric deposition, anthropogenic waste, and leaching. There is growing evidence of the ecotoxicity of microplastics to soil decomposers. Soil animals and microorganisms are the main decomposers of plant litter, and their interactions play important roles in determining the terrestrial biochemical cycle. However, how emerging microplastics in forests affect the influence of soil animals on the fungal community in decomposed litter is still unclear. Here, by constructing a rigorous mesocosm experiment, we investigated soil enzyme activities and the variation in fungal community characteristics in the leaf litter of a deciduous tree, Lindera glauca, which was decomposed by contrasting decomposer structures (with or without soil animals) under different contamination conditions (with or without microplastic contamination), aiming to determine the impacts of these factors on litter decomposition. We found that soil animals can significantly depress the litter decomposition rate by reducing fungal diversity and largely changing the community structure in the litter. However, these critical changes caused by soil animals were inhibited in the mesocosms contaminated with high-density polyethylene microplastics (HDPE−MPs), during which soil animal activities were significantly reduced. These findings represent a step forward in illustrating the potential effect of emerging contamination stress on forest litter decomposition and biogeochemical cycles under global environmental change.

Assessment of Molecular Diversity and Population Structure of Pakistani Mulberry Accessions Using Retrotransposon-Based DNA Markers

Both morphological and molecular markers have been extensively used to evaluate genetic diversity; however, molecular markers are considered more reliable and can lead to improved reproductive efficiency. This study utilized inter-primer binding site (iPBS) markers to examine the genetic diversity and population structure of thirty mulberry accessions from the districts of Sahiwal and Faisalabad, Pakistan. These mulberry accessions belonged to three species: Morus nigra (n = 13), Morus alba (n = 12), and Morus rubra (n = 5). The use of nine iPBS primers in this study provided a comprehensive understanding of genetic diversity among the selected mulberry accessions. Nine iPBS primers were used in the study and generated 431 bands with allelic frequencies ranging from 21 to 75 and band sizes from 200 to 1500 base pairs. The primer 2230 showed the highest polymorphic information content (PIC) value of 0.47 and the highest Shannon’s information index (I = 0.53). The Morus nigra accessions had the highest levels of expected heterozygosity (He = 0.30), unbiased expected heterozygosity (µHe = 0.33), and Shannon’s information index (I = 0.45). The molecular variance analysis (AMOVA) revealed a high degree of genetic variation, as estimated by the pairwise PhiPT value of 0.21, which was significant at the p < 0.001 *** level. The neighbor joining tree, principal coordinate analysis, and structure analysis grouped the 30 mulberry accessions into four main clusters. The distinct grouping of accessions SWLS14, SWLS6, FSDS30, and SWLS7 validated their notable genetic distinctiveness. Overall, these findings contribute valuable insights into the genetic landscape of mulberry accessions, which are essential for conservation and breeding strategies.

Counting List Homomorphisms from Graphs of Bounded Treewidth: Tight Complexity Bounds

The goal of this work is to give precise bounds on the counting complexity of a family of generalized coloring problems (list homomorphisms) on bounded-treewidth graphs. Given graphs G , H , and lists L (v) ⊆ V(H) for every v ∈ V(G) , a f:V(G) → V(H) that preserves the edges (i.e., uv ∈ E(G) implies f(u)f(v) ∈ E(H) ) and respects the lists (i.e., f(v) ∈ L(v) ). Standard techniques show that if G is given with a tree decomposition of width t , then the number of list homomorphisms can be counted in time |V(H)| t ⋅ n 𝒪(1) . Our main result is determining, for every fixed graph H , how much the base |V(H)| in the running time can be improved. For a connected graph H , we define irr( H ) in the following way: if H has a loop or is nonbipartite, then irr( H ) is the maximum size of a set S⊆ V(H) where any two vertices have different neighborhoods; if H is bipartite, then irr( H ) is the maximum size of such a set that is fully in one of the bipartition classes. For disconnected H , we define irr( H ) as the maximum of irr( C ) over every connected component C of H . It follows from earlier results that if irr( H )=1, then the problem of counting list homomorphisms to H is polynomial-time solvable, and otherwise it is #P-hard. We show that, for every fixed graph H , the number of list homomorphisms from (G,L) to H — can be counted in time \(\operatorname{irr}(H)^t\cdot n^{\mathcal {O}(1)}\) if a tree decomposition of G having width at most t is given in the input, and, — given that \(\operatorname{irr}(H)\ge 2\) , cannot be counted in time \((\operatorname{irr}(H)-\varepsilon)^t\cdot n^{\mathcal {O}(1)}\) for any \(\varepsilon \gt 0\) , even if a tree decomposition of G having width at most t is given in the input, unless the Counting Strong Exponential-Time Hypothesis (#SETH) fails. Thereby, we give a precise and complete complexity classification featuring matching upper and lower bounds for all target graphs with or without loops.

Fast simulation of planar Clifford circuits

A general quantum circuit can be simulated classically in exponential time. If it has a planar layout, then a tensor-network contraction algorithm due to Markov and Shi has a runtime exponential in the square root of its size, or more generally exponential in the treewidth of the underlying graph. Separately, Gottesman and Knill showed that if all gates are restricted to be Clifford, then there is a polynomial time simulation. We combine these two ideas and show that treewidth and planarity can be exploited to improve Clifford circuit simulation. Our main result is a classical algorithm with runtime scaling asymptotically as nω/2<n1.19 which samples from the output distribution obtained by measuring all n qubits of a planar graph state in given Pauli bases. Here ω is the matrix multiplication exponent. We also provide a classical algorithm with the same asymptotic runtime which samples from the output distribution of any constant-depth Clifford circuit in a planar geometry. Our work improves known classical algorithms with cubic runtime.A key ingredient is a mapping which, given a tree decomposition of some graph G, produces a Clifford circuit with a structure that mirrors the tree decomposition and which emulates measurement of the corresponding graph state. We provide a classical simulation of this circuit with the runtime stated above for planar graphs and otherwise ntω−1 where t is the width of the tree decomposition. Our algorithm incorporates two subroutines which may be of independent interest. The first is a matrix-multiplication-time version of the Gottesman-Knill simulation of multi-qubit measurement on stabilizer states. The second is a new classical algorithm for solving symmetric linear systems over F2 in a planar geometry, extending previous works which only applied to non-singular linear systems in the analogous setting.

On tree decompositions whose trees are minors

AbstractIn 2019, Dvořák asked whether every connected graph has a tree decomposition so that is a subgraph of and the width of is bounded by a function of the treewidth of . We prove that this is false, even when has treewidth 2 and is allowed to be a minor of .

Phylogenetic Relationships of Isognomon (Lightfoot, 1786) Oysters from North Sulawesi, Indonesia

The Isognomon (Lightfoot, 1786) is a genus of oysters found in various coastal ecosystems throughout the world. Along with other bivalves, it performs significant ecological functions in marine ecosystems by providing food and habitat for fish and invertebrate habitats, filtering water, and protecting shorelines. Taxonomic classification of the Isognomon oyster can be challenging due to the varied or cryptic phenotypic characters, particularly shell characters. In this study, two specimens with different shell characters of Isognomon oyster were collected from mangrove waters in Likupang, North Sulawesi, Indonesia, and subjected to molecular analysis to determine their identity. The mitochondrial cytochrome C oxidase subunit I (COI) gene was utilized as a primer for this purpose, and the genetic distance and phylogenetic position of the two specimens were determined by comparing them with the GenBank database. The Basic Local Alignment Search Tool (BLAST) revealed that the two specimens were of belonged to Isognomon ephippium, with a similarity of 99.84%. The genetic distance between the two specimens was calculated using the Tamura Nei model and found to be 0.00, while the genetic distance between I. ephippium and other species in the Isognomon genus ranged from 0.00 to 0.14. The results of the Neighbor Joining (NJ) tree analyses showed that the two specimens clustered together with I. ephippium, which was divided into two distinct clades with a strong bootstrap value of 100 at the node

A comprehensive DNA barcoding of Indian freshwater fishes of the Indus River system, Beas

The Beas River is one of the important rivers of the Indus River system located in Himachal Pradesh, India, that harbors a diverse range of freshwater fish species. The present study employed COI gene to investigate the ichthyofaunal diversity of river Beas. Through the sequencing of 203 specimens from Beas River, we identified 43 species, belonging to 31 genera, 16 families, and 10 orders. To analyze the genetic divergence and phylogeny of identified species, 485 sequences of Indian origin were retrieved from BOLD, resulting in a dataset of 688 sequences. Our findings consistently revealed a hierarchical increase in the mean K2P genetic divergence within species (0.80%), genus (9.06%), and families (15.35%). Automated Barcode Gap discovery, Neighbour Joining, and Bayesian inference consensus tree methodologies were employed to determine the putative species and their phylogeny, successfully delimiting most of the species with only a few exceptions. The results unveiled six species exhibiting high intra-species divergence (> 2%), suggesting the presence of sibling species and falsely identified sequences on online databases. The present study established the first DNA barcoding-based inventory of freshwater fish species in the Beas River providing comprehensive insights into economically exploited endangered and vulnerable species. In order to ensure the sustainable use of aquatic resources in the Beas River, we recommend the implementation of species measures to protect biodiversity and genetic resources.

Approximating Sparsest Cut in Low-treewidth Graphs via Combinatorial Diameter

The fundamental Sparsest Cut problem takes as input a graph G together with edge capacities and demands and seeks a cut that minimizes the ratio between the capacities and demands across the cuts. For n -vertex graphs G of treewidth k , Chlamtáč, Krauthgamer, and Raghavendra (APPROX’10) presented an algorithm that yields a factor- \(2^{2^k}\) approximation in time \(2^{O(k)} \cdot n^{O(1)}\) . Later, Gupta, Talwar, and Witmer (STOC’13) showed how to obtain a 2-approximation algorithm with a blown-up runtime of \(n^{O(k)}\) . An intriguing open question is whether one can simultaneously achieve the best out of the aforementioned results, that is, a factor-2 approximation in time \(2^{O(k)} \cdot n^{O(1)}\) . In this article, we make significant progress towards this goal via the following results: (i) A factor- \(O(k^2)\) approximation that runs in time \(2^{O(k)} \cdot n^{O(1)}\) , directly improving the work of Chlamtáč et al. while keeping the runtime single-exponential in k . (ii) For any \(\varepsilon \in (0,1]\) , a factor- \(O(1/\varepsilon ^2)\) approximation whose runtime is \(2^{O(k^{1+\varepsilon }/\varepsilon)} \cdot n^{O(1)}\) , implying a constant-factor approximation whose runtime is nearly single-exponential in k and a factor- \(O(\log ^2 k)\) approximation in time \(k^{O(k)} \cdot n^{O(1)}\) . Key to these results is a new measure of a tree decomposition that we call combinatorial diameter , which may be of independent interest.

On [formula omitted]-stable locally checkable problems parameterized by mim-width

In this paper we continue the study of locally checkable problems under the framework introduced by Bonomo-Braberman and Gonzalez in 2020, by focusing on graphs of bounded mim-width. We study which restrictions on a locally checkable problem are necessary in order to be able to solve it efficiently on graphs of bounded mim-width. To this end, we introduce the concept of d-stability of a check function. The related locally checkable problems contain large classes of problems, among which we can mention, for example, LCVP problems. We give an algorithm showing that these problems are XP when parameterized by the mim-width of a given binary decomposition tree of the input graph , that is, that they can be solved in polynomial time given a binary decomposition tree of bounded mim-width. We explore the relation between d-stable locally checkable problems and the recently introduced DN logic (Bergougnoux, Dreier and Jaffke, 2022), and show that both frameworks model the same family of problems. We include a list of concrete examples of d-stable locally checkable problems whose complexity on graphs of bounded mim-width was open so far.

Distributed Augmentation, Hypersweeps, and Branch Decomposition of Contour Trees for Scientific Exploration.

Contour trees describe the topology of level sets in scalar fields and are widely used in topological data analysis and visualization. A main challenge of utilizing contour trees for large-scale scientific data is their computation at scale using highperformance computing. To address this challenge, recent work has introduced distributed hierarchical contour trees for distributed computation and storage of contour trees. However, effective use of these distributed structures in analysis and visualization requires subsequent computation of geometric properties and branch decomposition to support contour extraction and exploration. In this work, we introduce distributed algorithms for augmentation, hypersweeps, and branch decomposition that enable parallel computation of geometric properties, and support the use of distributed contour trees as query structures for scientific exploration. We evaluate the parallel performance of these algorithms and apply them to identify and extract important contours for scientific visualization.

Appliance Identification via Combinatorial Fusion Analysis-Assisted Bayesian-Optimized Classifier

Non-Intrusive Load Monitoring (NILM) aids in estimating the individual energy consumed by different appliances using aggregate signals measured at point of common connection. This paper explores various unexplored aspects concerning load classification in NILM. Rich feature sets are extracted from load signatures using auto-correlogram, Variational Mode Decomposition (VMD), and Wavelet Packet Tree (WPT) decomposition. Typically, previous works employ a single Feature Selection (FS) technique to earmark the relevant features for improved performance of NILM. However, each FS technique behaves uniquely, and the effects of using different FS techniques or their combinations have yet to be explored for NILM. Hence, Combinatorial Fusion Analysis (CFA), which surpasses the performance of a single FS technique by considering an optimal combination of multiple FS techniques, is used in this paper. The final selected features are fused and provided to a classifier. Rather than manually tuning or performing an exhaustive search for setting the classifier’s hyper-parameters, as prevalent in previous works, the present work utilizes Bayesian Optimization (BO) comprising surrogate and acquisition functions. The proposed work’s efficacy is validated on publicly available data-sets in a host computer and an embedded device, namely, R-Pi, via comparison with previous works.

Fishing for the gonadotropin releasing hormone from the genome of Salminus brasiliensis (Characiformes: Bryconidae).

The technological development of tools that enable the spawning of different native species is paramount to enable ex situ conservation initiatives, as well as providing means for commercial hatchery of threatened fish which, in turn, relieve fisheries pressure over wild stocks. Neotropical migratory freshwater fish depend on hormonal induction for spawning in hatcheries, through expensive methods of limited efficiency. Salminus brasiliensis is one of the largest Neotropical freshwater fish, a piscivorous top-predator, prized in angling, highly valued in the market, and appreciated in gastronomy. Teleost fish have either, two or three GnRH paralogous genes: GnRH1, GnRH2 and the GnRH3. The expression products of these paralogous isoforms consist of a larger prepro-GnRH polypeptide, which undergoes post-translational proteolytic processing to yield the active decapeptide hormone. There is increasing interest in characterizing and understanding these neuropeptides, because of its practical application in hatchery spawning. We present the characterization of GnRH1's coding sequence for the prepro-GnRH1 polypeptide of S. brasiliensis. An annotation from a genomic assembly was used for searching for GnRH paralogues, based on data from anonymous predicted transcripts. The sequence retrieved for GnRH1 was then used as a query for searching the uncharacterized GnRH paralogues from full genomes of Characiformes deposited at NCBI. The S. brasiliensis GnRH1 gene sequence retrieved was targeted for PCR and submitted to Sanger sequencing, allowing for its confirmation. It spans 423 bp (exon 1: 128 bp; intron: 161 bp; and exon 2: 1134 bp), with open reading frames coding for 264 and 88 amino acids, respectively. The different variants retrieved for the prepro-GnRH (1, 2 and 3) from Characiformes genomes and deposited sequences from NCBI grouped in three distinct clades in a neighbor joining tree, each forming a monophyletic branch and with the S. brasiliensis sequences nested within the expected groups. Here we observed a variation at a proteolytic site (GKR→GRR), reported as highly conserved in vertebrates up to now, that can potentially alter the cleavage site and modify the peptide topology. This work has characterized, for the first time, the sequence of the GnRH1 coding for its prepro-GnRH peptide, for a member of the Charaficormes order. This will help to promote research and development of tools for broodstock spawning and environmental management of S. brasiliensis and related migratory fish.

An Analytic Hierarchy Process based approach for assessing the performance of photovoltaic solar power plants

The key performance indicators are crucial for monitoring the performance of photovoltaic solar plants, thus significantly enhancing their overall efficiency. This critical role underscores the importance of making informed decisions regarding the evaluation criteria of these indicators, which would streamline the PV installations evaluation process. Despite the significant importance of this role, the weighting and prioritization of KPI selection criteria in the specific field of solar plants have not been addressed in the literature. The article aims to address this gap by developing a methodology that identifies the most relevant aspects for evaluating the performance of photovoltaic plants, while taking into account specific needs and evaluation contexts, integrating a hierarchical decomposition tree of criteria, which serves as a framework to guide criterion prioritization through collaborative weighting with the thematic team. This methodology incorporates the Analytic Hierarchy Process, a multicriteria analysis approach designed to assist decision-makers in addressing their specific needs, ensuring optimal alignment with the characteristics of the optimization problem, whether it involves single or multiple objectives.

Molecular Characterization of Nepalese Indigenous Chicken, Sakini, Based on Mitochondrial DNA Displacement (D)-loop Sequences

This study is the first time to perform molecular characterization in indigenous chickens, Sakini, of Nepal for studying genetic diversity and its relationship with its assumed progenitors. The first 522 nucleotides of hypervariable I (HVI) segment of the D-loop from 33 individuals were PCR amplified and subsequently sequenced. Fourteen haplotypes out of 33 sequences were identified from 20 polymorphic sites. Haplotype (gene) diversity (Hd) is 0.813 with SD 0.065 and nucleotide diversity (Pi) is 0.00525 with SD 0.00091. The neighbour joining tree indicated that Red Jungle Fowl from India is the progenitor of the Nepalese Sakini chicken. NETWORK analysis revealed that it can be grouped into four distinct Haplogroups (A1, E1, E2, and E3) respectively. Seventeen individuals belonged to E1, eight to E3, seven to E2, and one to A1. The high mitochondrial D-loop diversity in Nepalese Sakini chicken with multiple maternal origins serves the scientific basis for the development of rational policies supporting conservation efforts and provides directions for future research for developing sustainable genetic improvement approaches.

Decomposition of bark beetle-attacked trees after mortality varies across forests

Bark beetle outbreaks result in massive amounts of deadwood which are a significant carbon reservoir that can impact forest ecosystems. Additionally, bark beetle-introduced symbiotic fungi in beetle-generated deadwood can increase termite presence and feeding. This could alter decomposition rates and saproxylic communities of bark beetle-generated deadwood. However, the fate of bark beetle-generated deadwood after tree mortality has received little attention despite the possibility bark beetles could alter decomposition processes and deadwood residence times at landscape scales following outbreaks. We tested the hypothesis that beetle-attacked trees decompose faster than unattacked trees. In Honduras, and Mississippi and Arizona, USA, we felled one recently bark beetle-attacked and one apparently healthy conspecific tree at each site that was cut into 120 experimental logs. Logs of each tree (attacked or unattacked) were assigned one of three metal mesh covering treatments: 1) fully covered to exclude all macroinvertebrates, 2) covered from above to exclude secondary bark beetle colonization, 3) no cover to allow all detrital food web organisms. Half of all logs at each site was collected after 1 and 2 years and the density loss, insect visual damage rating, and abundance of termites, ants, and beetles was measured. Bark beetle attack had the largest impacts at higher latitude and western forests: density loss between attacked and unattacked logs was consistently higher in Arizona (Flagstaff), initially slower (year 1) in Mississippi, but faster between years 1 and 2 resulting in similar density loss after 2 years, and did not differ in Honduras. Saproxylic insect wood damage rating accounted for 30% of the variation in decomposition across sites. Thus, decomposition rates of deadwood following bark beetle attack are highly variable across their geography and likely reflect important interactions among saproxylic organisms. This has implications for forest ecology and management including forest modeling with regard to carbon cycling and maintaining biodiversity of saproxylic organisms.

Bayesian Optimization in the Latent Space of a Variational Autoencoder for the Generation of Selective FLT3 Inhibitors.

The process of drug design requires the initial identification of compounds that bind their targets with high affinity and selectivity. Advances in generative modeling of small molecules based on deep learning are offering novel opportunities for making this process faster and cheaper. Here, we propose an approach to achieve this goal, where predictions of binding affinity are used in conjunction with the Junction Tree Variational Autoencoder (JTVAE) whose latent space is used to facilitate the efficient exploration of the chemical space using a Bayesian optimization strategy. The exploration identifies small molecules predicted to have both high affinity and high selectivity by using an objective function that optimizes the binding to the target while penalizing the binding to off-targets. The framework is demonstrated for FMS-like tyrosine kinase 3 (FLT3) and shown to predict small molecules with predicted affinity and selectivity comparable to those of clinically approved drugs for this target.