Challenging Complexity with Simplicity: Rethinking the Role of Single-Step Models in Computer-Aided Synthesis Planning.

MolMiner: You Only Look Once for Chemical Structure Recognition.

Advances and Challenges in De Novo Drug Design Using Three-Dimensional Deep Generative Models.

Prediction of liquid-liquid phase separating proteins using machine learning.

Learning to design drug-like molecules in three-dimensional space using deep generative models.

A transferable deep learning approach to fast screen potential antiviral drugs against SARS-CoV-2.

Super Edge-Connected Linear Hypergraphs.

Analysis of protein features and machine learning algorithms for prediction of druggable proteins.

CavityPlus: a web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction.

Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction.

Statistical Analysis and Prediction of Covalent Ligand Targeted Cysteine Residues.

PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database.

Computational Multitarget Drug Design.

Molecular Graph Encoding Convolutional Neural Networks for Automatic Chemical Feature Extraction.

Sequence-based prediction of protein protein interaction using a deep-learning algorithm.

Deep Learning for Drug-Induced Liver Injury.

<i>De Novo</i> Design of Multitarget Ligands with an Iterative Fragment-Growing Strategy.

iDrug: a web-accessible and interactive drug discovery and design platform.

LigBuilder 2: A Practical <i>de Novo</i> Drug Design Approach.

A Large Descriptor Set and a Probabilistic Kernel-Based Classifier Significantly Improve Druglikeness Classification.

Improving the Quality of 3D-QSAR by Using Flexible-Ligand Receptor Models.

Virtual Screening of Novel Noncovalent Inhibitors for SARS-CoV 3C-like Proteinase.

3D-QSAR and Receptor Modeling of Tyrosine Kinase Inhibitors with Flexible Atom Receptor Model (FLARM).