Computational Drug Discovery Methods

A Robust Computational Framework for the Optimization of CDK7 Inhibitors as Promising Cancer Therapy

This study designs non-covalent CDK7 inhibitors using computational methods to improve selectivity and safety compared to THZ1.

AMODO-EO: A Practical Framework for Adaptive ObjectiveDiscovery in Multi-Objective Drug Optimization

AMODO-EO discovers and integrates new optimization criteria during drug discovery, improving multi-objective optimization by revealing hidden molecular relationships.

IN SILICO DRUG DISCOVERY FOR NDM -1 METALLO β LACTAMASE INHIBITORS for KLEBSIELLA PUEUMONIAE

This study uses in silico methods to identify potential inhibitors for NDM-1 metallo-β-lactamase in K. pneumoniae, finding two sulfonamide compounds as lead candidates.

Structure-based Generation of a Secondary Nucleation Inhibitor in alpha-Synuclein Aggregation Using a Conditional Diffusion Model

The paper uses a conditional diffusion model to design a small molecule inhibitor of secondary nucleation in alpha-synuclein aggregation, showing it effectively inhibits fibril proliferation.

DockFormer: Affinity Prediction and Flexible Docking with Pair Transformer

DockFormer predicts protein-small molecule binding affinity and structure using a flexible docking approach, outperforming existing methods on benchmark datasets.

Machine Learning-Based Bioactivity Prediction of Potential PPAR-γ Agonists for the Management of Diabetes

The paper uses machine learning to predict PPAR-γ agonist bioactivity, achieving an R2 score of 0.83 using molecular descriptors from ChEMBL data.

Scaling Molecular Representation Learning with Hierarchical Mixture-of-Experts

This paper introduces H-MoE, a hierarchical MoE model for molecular representation learning that improves generalization by incorporating scaffold-level routing and curriculum learning.

OptiRanker: an Advanced Tool for Simulation and Comparative Analysis of Drug Prioritization Algorithms in in-Vivo Trials

OptiRanker simulates and compares drug prioritization algorithms to rank their predictive accuracy, enabling efficient in vivo trial design for personalized medicine.

BioScientist Agent: Designing LLM-Biomedical Agents with KG-Augmented RL Reasoning Modules for Drug Repurposing and Mechanistic of Action Elucidation

The paper introduces BioScientist Agent, a framework combining a biomedical knowledge graph, variational graph auto-encoder, reinforcement learning, and LLMs to improve drug repurposing and mechanistic reasoning.

Q-MOL: High Fidelity Platform for In Silico Drug Discovery and Design

Q-MOL improves drug discovery by accurately predicting ligand binding sites on flexible and disordered proteins, validated across diverse targets and experimental settings.

Identification of novel drug-drug interactions: towards precision medicine in women's reproductive health

This paper predicts novel drug-drug interactions (DDIs) in women's reproductive health, identifying 2,991 new interactions and their clinical implications.

Targeting the CAPON-NOS Axis: A Computational Strategy for Small Molecule Modulator Discovery

This paper identifies two small-molecule hits targeting the nNOS/CAPON interface using virtual screening and introduces computational tools for structure-based drug discovery.

Target Preference Maps: A machine learning model generalizing transferable drug-receptor interactions and guiding drug discovery

The paper introduces a machine learning model that learns spatial preference maps for drug-receptor interactions to guide drug discovery.

Repurposing of Anti-infectives for the Management of Onchocerciasis using Machine Learning and Protein Docking Studies

This study uses machine learning and molecular docking to identify 14 anti-infective drugs that may be repurposed for onchocerciasis treatment, showing binding affinities similar to existing drugs.

Leveraging Large Language Models for Literature-Driven Prioritization of Protein Binding Pockets

This paper combines geometric pocket detection with large language models to prioritize protein binding pockets for drug discovery, using a curated dataset and automated pipeline.

Single Cell Profiling Distinguishes Leukemia-Selective Chemotypes

The paper screens a compound library to identify leukemia-selective chemotypes using single-cell profiling and finds that certain rocaglate derivatives selectively induce DNA damage in leukemia cells.

Deep learning molecular interaction motifs from Receptor structure alone

This paper introduces MotifGen, a deep learning model that predicts molecular interaction motifs from receptor structures, enabling peptide binder design and small-molecule binding site prediction.

Hybrid protein-ligand binding residue prediction with protein language models: Does the structure matter?

This study evaluates a hybrid model combining protein language models and graph neural networks for predicting protein-ligand binding sites, showing that structural information improves performance but complex pLMs can achieve good results without it.

A Structure-Based Computational Pipeline for Broad-Spectrum Antiviral Discovery

This paper introduces a computational pipeline for identifying broad-spectrum antivirals by analyzing viral sequences, predicting ligand poses, and scoring protein-ligand complexes, validated on SARS-CoV-2 and MERS-CoV datasets.

Identification of Antiviral Drug Candidates Against Monkeypox DNA Polymerase and Profilin-like Protein A42R Utilizing an In-Silico Approach

This study identifies potential antiviral drug candidates against MPXV DNA polymerase and A42R protein using in-silico methods, highlighting compounds with high docking scores and stable ligand-protein complexes.

BioTransformer4.0 a comprehensive computational tool for small molecule metabolism prediction

BioTransformer4.0 predicts small molecule metabolites using integrated knowledge-based and machine learning approaches, achieving high recall on benchmark datasets.

DrugPT: A Flexible Framework for Integrating Gene and Chemical Representations in Perturbation Modeling

DrugPT predicts gene expression changes after drug treatment using a Transformer-based framework integrating gene and chemical representations.

Spatial Needleman-Wunsch: A Deterministic Dynamic Programming Framework for Molecular Docking

This paper introduces a deterministic dynamic programming framework for molecular docking that improves reproducibility and interpretability compared to stochastic methods.

A Multi-Modal Genomic Knowledge Distillation Framework for Drug Response Prediction

This paper proposes a framework that transfers knowledge from multi-modal genomic data to gene expression-only models to improve drug response prediction.

HTS-Oracle: A Retrainable AI Platform for High-Confidence Hit Identification Across Difficult-to-Drug Targets

HTS-Oracle improves hit identification for difficult-to-drug targets by using a deep learning platform that integrates molecular embeddings and cheminformatics features, achieving an 8.4% hit rate with reduced screening costs.

Transfer learning across molecular graphs for predicting protein-ligand affinities and their changes upon mutations

This paper introduces DDMuffin, a deep learning framework for predicting protein-ligand binding affinities and mutation-induced changes, showing strong performance on benchmark datasets and offering insights for drug design.

SHARP: Generating Synthesizable Molecules via Fragment-based Hierarchical Action-space Reinforcement Learning for Pareto Optimization

SHARP generates drug-like molecules using reinforcement learning with a fragment-based action space, improving synthesizability and binding affinity over existing methods.

High-efficiency discovery and structure-activity-relationship analysis of non-substrate-based covalent inhibitors of S-adenosylmethionine decarboxylase

This study enhances a computational protocol for discovering covalent inhibitors of AdoMetDC by integrating quantum chemistry calculations, achieving a 70.6% hit rate in screening 17 compounds.

Small Molecule-Based Blockade of CD28 Suppresses T Cell Costimulation Across Cellular and Mucosal Co-culture Models

This paper identifies a novel druggable site on CD28 and discovers 22VS, a small molecule inhibitor that suppresses T cell activation in co-culture models, with favorable pharmacokinetic properties.

Design in Voxel Space Decode in SMILES Space: Plixer Generates Drug-Like Molecules for Protein Pockets

Plixer generates drug-like molecules for protein pockets using a two-stage model that decouples spatial interaction learning from chemical grammar learning.