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Drug Discovery

32 papers with code ยท Medical

Drug discovery is the task of applying machine learning to discover new candidate drugs.

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Latest papers without code

DeepPCM: Predicting Protein-Ligand Binding using Unsupervised Learned Representations

ICLR 2020

We apply this reasoning to propose a novel proteochemometric modeling methodology which, for the first time, uses embeddings generated via unsupervised representation learning for both the protein and ligand descriptors.

DRUG DISCOVERY UNSUPERVISED REPRESENTATION LEARNING

Antifragile and Robust Heteroscedastic Bayesian Optimisation

ICLR 2020

Bayesian Optimisation is an important decision-making tool for high-stakes applications in drug discovery and materials design.

BAYESIAN OPTIMISATION DECISION MAKING DRUG DISCOVERY

VIMPNN: A physics informed neural network for estimating potential energies of out-of-equilibrium systems

ICLR 2020

Our method is extensively evaluated on a augmented version of the QM9 dataset that includes unstable molecules, as well as a new dataset of infinite- and finite-size crystals, and is compared with the Message Passing Neural Network (MPNN).

DRUG DISCOVERY

Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation

17 Oct 2019

Bayesian optimisation is an important decision-making tool for high-stakes applications in drug discovery and materials design.

BAYESIAN OPTIMISATION DECISION MAKING DRUG DISCOVERY

Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures

3 Sep 2019

We present a method to encode and decode the position of atoms in 3-D molecules from a dataset of nearly 50, 000 stable crystal unit cells that vary from containing 1 to over 100 atoms.

DRUG DISCOVERY TEXT GENERATION

Triclustering of Gene Expression Microarray Data Using Coarse-Grained Parallel Genetic Algorithm

31 Aug 2019

Microarray data analysis is one of the major area of research in the field computational biology.

DRUG DISCOVERY

Gated Graph Recursive Neural Networks for Molecular Property Prediction

31 Aug 2019

Molecule property prediction is a fundamental problem for computer-aided drug discovery and materials science.

DRUG DISCOVERY

Deep Learning for Estimating Synaptic Health of Primary Neuronal Cell Culture

29 Aug 2019

Understanding the morphological changes of primary neuronal cells induced by chemical compounds is essential for drug discovery.

DRUG DISCOVERY

DeepScaffold: a comprehensive tool for scaffold-based de novo drug discovery using deep learning

20 Aug 2019

Furthermore, the generated compounds were evaluated by molecular docking in DRD2 targets and the results demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold and de novo drug design of potential drug candidates with specific docking scores.

DRUG DISCOVERY

Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction

15 Aug 2019

Predicting drug-target interactions (DTI) is an essential part of the drug discovery process, which is an expensive process in terms of time and cost.

DRUG DISCOVERY