Developed accurate empirical potentials for Si:H nanowires using multi-fidelity Gaussian process.
problem Accurate modeling of Si:H nanowires using fast but inaccurate empirical potentials and slow but accurate first-principle calculations.
method Employed multi-fidelity Gaussian process regression to integrate low-fidelity empirical potential data with high-fidelity first-principle calculations.
result Demonstrated the accuracy of developed empirical potentials for Si:H nanowires.
Deep learning predicts protein-small molecule binding.
problem Insufficient benchmark datasets for structure-based virtual screening.
method Learnable atom convolution, non-linear transformation, inner-product for binding potential prediction.
result New benchmark dataset improves testing of structure-based virtual screening methods.
Deep models learn biases from datasets, hindering understanding of binding mechanisms.
problem Dataset biases prevent deep models from revealing fragment logic of protein-ligand binding.
method Attribution method to identify and exploit dataset biases in neural networks.
result Deep models can be fooled into learning spurious correlations from biased datasets.
NucleusDiff models atomic nuclei interactions to prevent separation violations in drug design.
problem Maintaining minimum pairwise distance between atoms to avoid separation violations in drug design.
method Enforces distance constraint between atomic nuclei and manifolds in a diffusion model.
result Reduces separation violations by up to 100.00% and enhances binding affinity by up to 22.16%.
We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalize eight kernels, such as the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We …
Active learning speeds up antibody affinity prediction.
problem Challenging to identify mutations enhancing antibody affinity.
method Iterative proposal of promising sequences for simulation.
result Accelerates search for improved binders.
iDeepA predicts RNA-protein binding sites from RNA sequences using a CNN with attention.
problem Predicting RNA-protein binding sites from raw RNA sequences efficiently.
method Attention based convolutional neural network (iDeepA) encoding RNA sequences into one-hot encoding, followed by a CNN with an attention mechanism.
result iDeepA achieves comparable performance to state-of-the-art methods on CLIP-seq data.
This abstract reviews recent methods for predicting protein-ligand binding affinity.
problem Predicting protein-ligand binding affinity for various applications in life sciences.
method Traditional and deep learning models for binding affinity prediction.
result Improved predictive performance of AI-driven models.
Study on binding numbers of tight contact structures on lens spaces L(n,1).
problem Determining the minimum number of binding components for tight contact structures on lens spaces.
method Using the d3-invariant, restrictions on planar monodromy factorizations, and the Durst-Kegel algorithm. result The binding number of universally tight contact structures on L(n,1) is equal to n. DeepDTA predicts drug-target binding affinities using deep learning.
problem Predicting the continuum of binding strength values between drugs and targets.
method Uses deep learning, specifically CNNs, to model 1D representations of drug and target sequences.
result Deep learning model outperforms state-of-the-art methods in predicting DT binding affinities.
The study shows examples of contact 3-manifold binding sums that fail to preserve certain properties.
problem Examples of contact 3-manifold binding sums that fail to preserve properties like tightness or symplectic fillability.
method Examples and proofs of vanishing Heegaard Floer contact invariant for Stein fillable manifolds.
result Binding sums of contact 3-manifolds do not preserve properties such as tightness or symplectic fillability.
WideDTA predicts drug-target binding affinity using text-based information.
problem Predicting drug-target binding affinity is a major challenge in drug discovery.
method WideDTA uses chemical and biological textual sequence information, including protein sequence, ligand SMILES, protein domains and motifs, and maximum common substructure words.
result WideDTA outperformed DeepDTA on the KIBA dataset, indicating the word-based sequence representation is a promising alternative.
New method shows links can be braided open book bindings.
problem Understanding fibered links and their bindings.
method Mutual arc presentations and braided open books.
result Every fibered link is the binding of a braided open book.
PANDA predicts protein binding affinity changes from sequences, outperforming existing methods.
problem Accurately predicting changes in protein binding affinity due to mutations.
method Sequence-based machine learning approach using protein sequence information.
result PANDA achieves higher Pearson correlation coefficients than existing methods.
Memory Matching Networks classify DNA sequences for protein binding sites.
problem Manual construction of DNA motifs is difficult due to their complexity.
method Memory Matching Networks (MMN) learn a dynamic memory bank of encoded motifs and match them to new sequences.
result MMN effectively classifies DNA sequences as protein binding or nonbinding sites.
We study an explicit construction of planar open books with four binding components on any three-manifold which is given by integral surgery on three component pure braid closures. This construction is general, indeed any planar open book with four binding components is given this way. Using this construction and resul…
We consider few-body bound state systems and provide precise definitions of Borromean and Brunnian systems. The initial concepts are more than a hundred years old and originated in mathematical knot-theory as purely geometric considerations. About thirty years ago they were generalized and applied to the binding of sys…
Deep learning model predicts protein-ligand binding modes from docking data.
problem Improving protein-ligand binding mode prediction accuracy.
method Dual-graph architecture with separate sub-networks for ligand topology and protein-ligand interactions.
result Deep learning model outperforms docking programs in binding mode prediction.
Let T denote a binding component of an open book (Σ,φ) compatible with a closed contact 3-manifold (M,ξ). We describe an explicit open book (Σ′,φ′) compatible with (M,ζ), where ζ is the contact structure obtained from ξ by performing a full Lutz twist along T. Here, (Σ′,φ′) is obtained from $(Σ, …
New model predicts protein-ligand binding affinity from atomic coordinates.
problem Predicting protein-ligand binding affinity using empirical scoring functions.
method Developed atomic convolutional neural network to learn chemical interactions directly from atomic coordinates.
result Atomic convolutional networks outperform or compete with cheminformatics methods in predicting binding free energy.
CNN scoring function predicts protein-ligand interactions.
problem Scoring protein-ligand interactions for drug discovery.
method Convolutional Neural Networks (CNN) for 3D protein-ligand interactions.
result CNN scoring function outperforms AutoDock Vina in ranking poses.
NeuralMD accelerates protein-ligand binding simulations 1Kx faster.
problem Accurate and efficient simulation of protein-ligand binding dynamics.
method Physics-informed multi-grained group symmetric framework with BindingNet and augmented neural differential equation solver.
result Achieves over 1Kx speedup and up to 15x reduction in reconstruction error compared to standard methods.
Prototype Matching Network (PMN) improves genomic TFBS prediction.
problem Predicting Transcription Factor Binding Sites (TFBSs) with hundreds of TFs as labels.
method Prototype Matching Network (PMN) that learns motif-like features and TF-TF interactions.
result PMN significantly outperforms baselines on a large TFBS dataset.
Deep learning predicts protein-ligand binding affinity with high accuracy.
problem Predicting protein-ligand binding affinity for drug discovery.
method 3D convolutional neural network trained on CASF and Astex Diverse Set benchmarks.
result Deep learning model outperformed classical scoring functions.
Introduces nested open books for manifold decomposition.
problem Decomposing manifolds using open book structures.
method Defines nested open books and their push-offs, applies to fibre connected sums.
result Explicitly describes open book decompositions of fibre connected sums.
This paper studies a subgroup of the Goeritz group related to Heegaard splittings induced by openbook decompositions.
problem Understanding the subgroup of the Goeritz group associated with Heegaard splittings from openbook decompositions.
method Analyzes the mapping class group of a 3-manifold, focusing on elements that preserve the binding and commute with the monodromy.
result Characterizes the Goeritz group subgroup as a quotient of specific mapping class groups and provides a criterion for certain elements.
Framework designs antiviral drugs using deep learning and RL.
problem Designing effective antiviral drugs for SARS-CoV-2.
method Deep learning framework with conditional molecular generator and RL.
result Framework generates more antiviral ligands than a VAE baseline.
PADME predicts drug-target interaction strengths using deep learning.
problem Challenges in drug-target interaction prediction, especially for cold-target problems.
method PADME uses deep neural networks to predict real-valued interaction strengths between compounds and proteins, handling cold-target problems.
result PADME consistently outperforms baseline methods on multiple datasets, including the ToxCast dataset.
siRF identifies transcription factor binding near enhancers in flies.
problem Identifying functional transcription factor binding near enhancers.
method Signed iterative random forests (siRF) for machine learning.
result Infers regulatory interactions among transcription factors and enhancers.
Consider a transverse knot which is the binding of an open book for the ambient contact manifold. In this paper, we show that the transverse invariants defined by Lisca, Ozsvath, Stipsicz, and Szabo (LOSS) are nonvanishing for such transverse knots. This is true regardless of whether or not the ambient contact structur…
We show that if (B,π) is an open book decomposition of a contact 3-manifold (Y,ξ), then the complement of the binding B has no Giroux torsion. We also prove the sutured Heegaard-Floer c-bar invariant of the binding of an open book is non-zero.
Paper warns against assuming all unreported compound-target binding profiles are negative.
problem Assumption of all unreported compound-target binding profiles being negative is unreliable.
method Proposes a framework to jointly recover unreported profiles and learn predictive models.
result Explicit recovery of unreported profiles improves prediction performance.
Paper presents a new model to predict peptide:MHC-II interactions.
problem Predicting peptide interactions with MHC-II for vaccine design and immune response understanding.
method Developed a trans-allelic prediction model using sequence and structural data.
result Model predicts interactions for all three human MHC-II loci and performs comparably to state-of-the-art methods.
Parametric insurance offers better risk-sharing in high-risk settings than traditional indemnity insurance.
problem High-risk environments where traditional indemnity insurance is unaffordable or ineffective.
method Comparison of excess-of-loss indemnity insurance and parametric insurance within a mean-variance framework, considering fixed costs and binding budget constraints.
result Parametric insurance yields higher welfare for risk-averse individuals, especially when indemnity insurance is impractical.
Computational approaches to transcription factor binding site identification have been actively researched for the past decade. Negative examples have long been utilized in de novo motif discovery and have been shown useful in transcription factor binding site search as well. However, understanding of the roles of nega…
BIND removes background noise from binary matrices, improving detection accuracy and fairness.
problem Real data often violates the i.i.d assumption for binary matrix entries, leading to inaccurate detection.
method BIND optimizes detection by estimating row- and column-wise mixture distributions and eliminating background noise.
result BIND effectively removes background noise and increases detection accuracy and fairness.
Language models fail to execute simple steps, showing gating and binding errors.
problem Procedural hallucinations in language models, failing to execute simple steps.
method Analyzed long-context binding tasks, identifying gating and binding errors.
result Procedural errors are due to gating and binding failures, with recency bias contributing to the latter.
Deep models generate and optimize DNA sequences for protein binding.
problem Designing DNA sequences with desired properties.
method Three approaches: GAN for synthetic sequences, activation maximization for design, and a combined method.
result Generated DNA sequences have superior properties to those in training data.
A new model explains protein interactions via electron delocalization.
problem Understanding how protein interactions affect each other.
method Quantized discrete differential geometry of n-simplices.
result Allosteric regulation follows from the model of interactions.
DeepRAM evaluates and selects the best deep learning architecture for DNA/RNA binding specificity prediction.
problem Selecting the best deep learning architecture for predicting DNA/RNA binding specificity.
method Systematic exploration of various deep learning architectures using deepRAM, an end-to-end deep learning tool.
result A k-mer embedding convolutional layer and recurrent layer architecture outperforms other methods.
Adaptive anchor methods improve multi-modal learning by balancing intra-modal and inter-modal information.
problem Fixed anchor methods limit multi-modal learning by over-reliance on a single modality and inadequate cross-modal correlation.
method Adaptive anchor methods using centroid-based anchors from all modalities.
result Adaptive anchor methods like CentroBind consistently outperform fixed anchor methods across various datasets.
SILVR generates new molecules fitting protein binding sites.
problem Generating novel small molecule compounds for drug design.
method Selective Iterative Latent Variable Refinement (SILVR) for diffusion-based molecule generation.
result SILVR can generate new molecules similar in shape to original fragments without protein knowledge.
Optimizes ligand binding poses using CNNs and atomic grids.
problem Improving the accuracy of docking predictions for drug discovery.
method Differentiable atomic grid representation, CNN for scoring and optimization.
result Iteratively-trained CNNs outperform single CNNs in optimizing poses.
In this note we define three invariants of contact structures in terms of open books supporting the contact structures. These invariants are the support genus (which is the minimal genus of a page of a supporting open book for the contact structure), the binding number (which is the minimal number of binding components…
An investor with constant relative risk aversion and an infinite planning horizon trades a risky and a safe asset with constant investment opportunities, in the presence of small transaction costs and a binding exogenous portfolio constraint. We explicitly derive the optimal trading policy, its welfare, and implied tra…
In the present paper we describe compatible open books for the fibre connected sum along binding components of open books, as well as for the fibre connected sum along multi-sections of open books. As an application the first description provides simple ways of constructing open books supporting all tight contact struc…
Given a closed binding curve γ of a surface Σ, any equivalence class of marked complete hyperbolic structure can be decomposed into polygons(possibly with a puncture) with sides being hyperbolic geodesic segments. When Σ is a one-holed torus and γ=A3B2, we show that any equivalence class of marked complete …
ChemBoost predicts protein-ligand binding affinity using SMILES syntax.
problem Predicting high affinity drug-target interactions from sequence similarity alone.
method ChemBoost uses SMILES syntax to represent ligands as documents and proteins as sequences or ligand-centric features. It learns chemical word embeddings and predicts affinities using eXtreme Gradient Boosting.
result ChemBoost outperforms state-of-the-art systems in predicting protein-ligand affinities.