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Found 6 papers, 2 papers with code
HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations
Publicly available collections of drug-like molecules have grown to comprise 10s of billions of possibilities in recent history due to advances in chemical synthesis.
Evaluating Point-Prediction Uncertainties in Neural Networks for Drug Discovery
In this paper, we examine UQ methods that estimate different sources of predictive uncertainty for NN models aiming at drug discovery.
High-Throughput Virtual Screening of Small Molecule Inhibitors for SARS-CoV-2 Protein Targets with Deep Fusion Models
Structure-based Deep Fusion models were recently shown to outperform several physics- and machine learning-based protein-ligand binding affinity prediction methods.
Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference
We present fusion models to benefit from different feature representations of two neural network models to improve the binding affinity prediction.
AMPL: A Data-Driven Modeling Pipeline for Drug Discovery
The ATOM Modeling PipeLine, or AMPL, extends the functionality of the open source library DeepChem and supports an array of machine learning and molecular featurization tools.
Distinguishing between Normal and Cancer Cells Using Autoencoder Node Saliency
With the trained autoencoder, we generate latent representations of a small dataset, containing pairs of normal and cancer cells of various tumor types.
