arXiv research

WideDTA predicts drug-target binding affinity using text-based information.

The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein-l…

Co-Diffusion predicts drug-target affinity by learning latent manifolds and diffusion, improving generalization.

GEFA predicts drug-target affinity using graph neural networks.

Identification of high affinity drug-target interactions is a major research question in drug discovery. Proteins are generally represented by their structures or sequences. However, structures are available only for a small subset of biomolecules and sequence similarity is not always correlated with functional similar…

This abstract reviews recent methods for predicting protein-ligand binding affinity.

In silico drug-target interaction (DTI) prediction is an important and challenging problem in biomedical research with a huge potential benefit to the pharmaceutical industry and patients. Most existing methods for DTI prediction including deep learning models generally have binary endpoints, which could be an oversimp…

PANDA predicts protein binding affinity changes from sequences, outperforming existing methods.

NucleusDiff models atomic nuclei interactions to prevent separation violations in drug design.

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.

Paper introduces IC-index to evaluate interaction prediction methods.

Empirical scoring functions based on either molecular force fields or cheminformatics descriptors are widely used, in conjunction with molecular docking, during the early stages of drug discovery to predict potency and binding affinity of a drug-like molecule to a given target. These models require expert-level knowled…

Deep generative model discovers inhibitors for unknown targets.

CogMol designs novel drug-like molecules for SARS-CoV-2 targets.

Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein-ligand binding an…

This paper presents regression models obtained from a process of blind prediction of peptide binding affinity from provided descriptors for several distinct datasets as part of the 2006 Comparative Evaluation of Prediction Algorithms (COEPRA) contest. This paper finds that kernel partial least squares, a nonlinear part…

Proposes a self-attention-based method for drug-target interaction prediction.

NeuralMD accelerates protein-ligand binding simulations 1Kx faster.

Structure based ligand discovery is one of the most successful approaches for augmenting the drug discovery process. Currently, there is a notable shift towards machine learning (ML) methodologies to aid such procedures. Deep learning has recently gained considerable attention as it allows the model to "learn" to extra…

Novel GNN predicts drug-target interactions using protein-ligand 3D structures.

Protein-ligand scoring is an important step in a structure-based drug design pipeline. Selecting a correct binding pose and predicting the binding affinity of a protein-ligand complex enables effective virtual screening. Machine learning techniques can make use of the increasing amounts of structural data that are beco…

The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto encoder and a convolutional classifier for feature manipulation and drug target interaction prediction. Two convolutional neural neworks are proposed wher…

Proposes a multi-view architecture for drug-target interaction prediction.

GCPNet improves molecular graph learning for protein structure and binding.

ISAAC audits deep models for drug-target interactions, revealing structural differences.

New DTI model using self-attention molecule representation outperforms state-of-the-art.

Two ML frameworks predict antibody properties using structural data.

DeepPurpose simplifies DL for DTI prediction.

SILVR generates new molecules fitting protein binding sites.

Motivation: Drug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy, and interpretability. Results: We present a seamless integration of domain knowledges and …

AntBO optimizes antibody design using Bayesian optimization for efficient and effective CDRH3 sequence generation.

Active learning has shown to reduce the number of experiments needed to obtain high-confidence drug-target predictions. However, in order to actually save experiments using active learning, it is crucial to have a method to evaluate the quality of the current prediction and decide when to stop the experimentation proce…

New method models aptamer libraries as Boltzmann-weighted graph ensembles for better affinity predictions.

We propose a novel transfer learning approach for orphan screening called corresponding projections. In orphan screening the learning task is to predict the binding affinities of compounds to an orphan protein, i.e., one for which no training data is available. The identification of compounds with high affinity is a ce…

Framework designs antiviral drugs using deep learning and RL.

RNA-binding proteins (RBPs) play crucial roles in many biological processes, e.g. gene regulation. Computational identification of RBP binding sites on RNAs are urgently needed. In particular, RBPs bind to RNAs by recognizing sequence motifs. Thus, fast locating those motifs on RNA sequences is crucial and time-efficie…

Study on binding numbers of tight contact structures on lens spaces $L(n,1)$.

The study shows examples of contact 3-manifold binding sums that fail to preserve certain properties.

New method shows links can be braided open book bindings.

New method for manifold topological learning avoids remeshing issues.

Study optimal policies under budget and coverage constraints.

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…

HiGraphDTI learns drug and target representations from molecular graphs to predict DTIs.

Deep learning model predicts protein-ligand binding modes from docking data.

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, …

One of the fundamental tasks in understanding genomics is the problem of predicting Transcription Factor Binding Sites (TFBSs). With more than hundreds of Transcription Factors (TFs) as labels, genomic-sequence based TFBS prediction is a challenging multi-label classification task. There are two major biological mechan…

We introduce the notion of a nested open book, a submanifold equipped with an open book structure compatible with an ambient open book, and describe in detail the special case of a push-off of the binding of an open book. This enables us to explicitly describe a natural open book decomposition of a fibre connected sum …