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24 dataset results for Drug Discovery

QM9 provides quantum chemical properties (at DFT level) for a relevant, consistent, and comprehensive chemical space of small organic molecules. This database may serve the benchmarking of existing methods, development of new methods, such as hybrid quantum mechanics/machine learning, and systematic identification of structure-property relationships.

55 PAPERS • 6 BENCHMARKS

Tox21

The Tox21 data set comprises 12,060 training samples and 647 test samples that represent chemical compounds. There are 801 "dense features" that represent chemical descriptors, such as molecular weight, solubility or surface area, and 272,776 "sparse features" that represent chemical substructures (ECFP10, DFS6, DFS8; stored in Matrix Market Format ). Machine learning methods can either use sparse or dense data or combine them. For each sample there are 12 binary labels that represent the outcome (active/inactive) of 12 different toxicological experiments. Note that the label matrix contains many missing values (NAs). The original data source and Tox21 challenge site is https://tripod.nih.gov/tox21/challenge/.

25 PAPERS • 5 BENCHMARKS

BACE (β-secretase enzyme)

The BACE dataset focuses on inhibitors of human beta-secretase 1 (BACE-1). It includes both quantitative (IC50 values) and qualitative (binary labels) binding results. The dataset comprises small molecule inhibitors across a wide range of affinities, spanning three orders of magnitude (from nanomolar to micromolar IC50 values). Specifically, it provides: 154 BACE inhibitors for affinity prediction. 20 BACE inhibitors for pose prediction. 34 BACE inhibitors for free energy prediction.

17 PAPERS • 4 BENCHMARKS

BBBP (Blood-Brain Barrier Penetration)

The BBBP dataset comes from a study focused on modeling and predicting the permeability of the blood-brain barrier. The BBBP dataset contains binary labels indicating whether a compound can penetrate the blood-brain barrier (BBB) or not. Researchers use this dataset to develop and evaluate machine learning methods for predicting BBB permeability. It’s a critical task because understanding which compounds can cross the BBB is essential for drug discovery and designing therapeutics for neurological conditions.

17 PAPERS • 5 BENCHMARKS

SIDER

SIDER contains information on marketed medicines and their recorded adverse drug reactions. The information is extracted from public documents and package inserts. The available information include side effect frequency, drug and side effect classifications as well as links to further information, for example drug–target relations.

16 PAPERS • 4 BENCHMARKS

HIV (Human Immunodeficiency Virus)

The HIV dataset was introduced by the Drug Therapeutics Program (DTP) AIDS Antiviral Screen, which tested the ability to inhibit HIV replication for over 40,000 compounds. Screening results were evaluated and placed into three categories: confirmed inactive (CI), confirmed active (CA), and confirmed moderately active (CM).

15 PAPERS • 5 BENCHMARKS

ClinTox

The ClinTox dataset compares drugs approved by the FDA and drugs that have failed clinical trials for toxicity reasons. The dataset includes two classification tasks for 1491 drug compounds with known chemical structures: (1) clinical trial toxicity (or absence of toxicity) and (2) FDA approval status.

14 PAPERS • 3 BENCHMARKS

QED

QED is a linguistically principled framework for explanations in question answering. Given a question and a passage, QED represents an explanation of the answer as a combination of discrete, human-interpretable steps: sentence selection := identification of a sentence implying an answer to the question referential equality := identification of noun phrases in the question and the answer sentence that refer to the same thing predicate entailment := confirmation that the predicate in the sentence entails the predicate in the question once referential equalities are abstracted away. The QED dataset is an expert-annotated dataset of QED explanations build upon a subset of the Google Natural Questions dataset.

14 PAPERS • 1 BENCHMARK

ToxCast (Toxicity Forecaster)

ToxCast is an initiative by the U.S. Environmental Protection Agency (EPA) aimed at predicting the potential toxicity of various chemical compounds. It involves high-throughput screening assays that evaluate thousands of chemicals across multiple biological endpoints. These endpoints cover a wide range of effects, including cell cycle disruptions, interactions with steroid receptors, and cytotoxicity.

12 PAPERS • 4 BENCHMARKS

BindingDB (The Binding Database)

BindingDB is a public, web-accessible database of measured binding affinities, focusing chiefly on the interactions of protein considered to be drug-targets with small, drug-like molecules. As of May 27, 2022, BindingDB contains 41,296 Entries, each with a DOI, containing 2,519,702 binding data for 8,810 protein targets and 1,080,101 small molecules. There are 5,988 protein-ligand crystal structures with BindingDB affinity measurements for proteins with 100% sequence identity, and 11,442 crystal structures allowing proteins to 85% sequence identity.You can also use BindingDB data through the Registry of Open Data on AWS: https://registry.opendata.aws/binding-db. This dataset using the split by TransformerCPI(doi.org/10.1093/bioinformatics/btaa524)

10 PAPERS • 2 BENCHMARKS

ESOL (Estimated SOLubility)

ESOL is a water solubility prediction dataset consisting of 1128 samples.

9 PAPERS • 3 BENCHMARKS

FreeSolv (Free Solvation)

The FreeSolv database offers a curated collection of experimental and calculated hydration-free energies for small molecules in water. It includes both experimental values obtained from prior literature and calculated values based on simulations. The goal is to provide accurate hydration-free energy data, which is essential for understanding solvation properties and interactions of molecules in aqueous environments.

9 PAPERS • 2 BENCHMARKS

MUV

The Maximum Unbiased Validation (MUV) dataset is a benchmark dataset selected from PubChem BioAssay. It was created by applying a refined nearest-neighbor analysis. The MUV dataset is specifically designed for the validation of virtual screening techniques.

9 PAPERS • 3 BENCHMARKS

PDBBind

The PDBbind database is a comprehensive collection of experimentally measured binding affinity data (Kd, Ki, and IC50) for the protein-ligand complexes deposited in the Protein Data Bank (PDB). It thus provides a link between energetic and structural information of protein-ligand complexes, which is of great value to various studies on molecular recognition occurring in biological systems.

9 PAPERS • 4 BENCHMARKS

DAVIS-DTA

Dataset Description: The interaction of 72 kinase inhibitors with 442 kinases covering >80% of the human catalytic protein kinome.

6 PAPERS • 3 BENCHMARKS

KIBA

Dataset Description: Toward making use of the complementary information captured by the various bioactivity types, including IC50, K(i), and K(d), Tang et al. introduces a model-based integration approach, termed KIBA to generate an integrated drug-target bioactivity matrix.

5 PAPERS • 1 BENCHMARK

LIT-PCBA(ALDH1)

LIT-PCBA(ALDH1) (ALDH1 target of LIT-PCBA Dataset)

Comparative evaluation of virtual screening methods requires a rigorous benchmarking procedure on diverse, realistic, and unbiased data sets. Recent investigations from numerous research groups unambiguously demonstrate that artificially constructed ligand sets classically used by the community (e.g., DUD, DUD-E, MUV) are unfortunately biased by both obvious and hidden chemical biases, therefore overestimating the true accuracy of virtual screening methods. We herewith present a novel data set (LIT-PCBA) specifically designed for virtual screening and machine learning. LIT-PCBA relies on 149 dose–response PubChem bioassays that were additionally processed to remove false positives and assay artifacts and keep active and inactive compounds within similar molecular property ranges. To ascertain that the data set is suited to both ligand-based and structure-based virtual screening, target sets were restricted to single protein targets for which at least one X-ray structure is available in

4 PAPERS • 1 BENCHMARK

LIT-PCBA(KAT2A)

LIT-PCBA(KAT2A) (KAT2A target of LIT-PCBA Dataset)

4 PAPERS • 1 BENCHMARK

LIT-PCBA(MAPK1)

LIT-PCBA(MAPK1) (MAPK1 target of LIT-PCBA Dataset)

4 PAPERS • 1 BENCHMARK

CausalBench

CausalBench is a comprehensive benchmark suite for evaluating network inference methods on large-scale perturbational single-cell gene expression data. CausalBench introduces several biologically meaningful performance metrics and operates on two large, curated and openly available benchmark data sets for evaluating methods on the inference of gene regulatory networks from single-cell data generated under perturbations. The datasets consists of over 200000 training samples under interventions.

3 PAPERS • NO BENCHMARKS YET

LIT-PCBA(ESR1_ant)

LIT-PCBA(ESR1_ant) (ESR1_ant target of LIT-PCBA Dataset)

3 PAPERS • 1 BENCHMARK

PCBA

PCBA dataset 11 is a collection of high-quality dose-response data, formulated as a multitask learning benchmark from 128 high-throughput screening (HTS) assays.

3 PAPERS • 2 BENCHMARKS

Lipophilicity (logd74)

The lipophilicity database refers to a collection of information related to the lipophilic properties of various molecules. Lipophilicity, also known as hydrophobicity, is a measure of how readily a substance dissolves in nonpolar solvents (such as oil) compared to polar solvents (such as water). In the context of drug discovery and pharmacology, understanding the lipophilicity of compounds is crucial because it affects their absorption, distribution, metabolism, and excretion (ADME) within the body.

2 PAPERS • 1 BENCHMARK

ImDrug

ImDrug is a comprehensive benchmark with an open-source Python library which consists of 4 imbalance settings, 11 AI-ready datasets, 54 learning tasks and 16 baseline algorithms tailored for imbalanced learning. It features modularized components including formulation of learning setting and tasks, dataset curation, standardized evaluation, and baseline algorithms. It also provides an accessible and customizable testbed for problems and solutions spanning a broad spectrum of the drug discovery pipeline such as molecular modeling, drug-target interaction and retrosynthesis.

1 PAPER • NO BENCHMARKS YET

Datasets

24 dataset results for Drug Discovery