lhkhiem28/ZINC20 dataset hosted on Hugging Face and contributed by the HF Datasets community

2 JSON dicts that list the connectivity features (key) ECFP4 (including the ECFP2) as detected by the GetMorganFingerprint function of the RDkit program. One files encompass all the 556,187 ECFP4 of the substances of ChEMBL25 as downloaded in September 2019 with 1,817,766 unique molecules. It is a large curated database of bioactive molecules. Here the values are 5 ChEMBL references that can be used to represent the fingerprint.

The second dict include the 1,156,416 ECFP(2 and 4) encountered in either the ZINC20 or ChEMBL25. ZINC is larger than ChEMBL and is based on commercially available compounds and not restricted to bioactive molecules. It encompass in proportion more inorganic and organometallic compounds than ChEMBL. We have used the already prepared version ZINC20-ML by Artem Cherkasov and Francesco Gentile with all the 1,006,651,037 ZINC20 molecules as of early March 2021. ZINC20-ML is available at https://files.docking.org/zinc20-ML/.

This Dataset contains year and district wise received, unusable and distributed stock data for all the indicators related to Family Planning, Maternal Health, and Immunization under HMIS for Punjab

Cathepsin L (CTSL) is a promising therapeutic target for metabolic disorders and COVID-19. However, there are still no clinically available CTSL inhibitors. Our objective is to develop an approach for the discovery of potential reversible covalent CTSL inhibitors. The authors combined Chemprop, a deep learning-based strategy, and the Schrödinger CovDock algorithm to identify potential CTSL inhibitors. First, they used Chemprop to train a deep learning model capable of predicting whether a molecule would inhibit the activity of CTSL and performed predictions on ZINC20 in-stock librarie (~9.2 million molecules). Then, they selected the top-200 predicted molecules and performed the Schrödinger covalent docking algorithm to explore the binding patterns to CTSL (PDB: 5MQY). The authors then calculated the binding energies using Prime MM/GBSA and examined the stability between the best two molecules and CTSL using 100ns molecular dynamics simulations. The authors found five molecules that showed better docking results than the well-known cathepsin inhibitor odanacatib. Notably, two of these molecules, ZINC-35287427 and ZINC-1857528743, showed better docking results with CTSL compared to other cathepsins. Our approach enables drug discovery from large-scale databases with little computational consumption, which will save the cost and time required for drug discovery.

ZINC is a free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable compounds in ready-to-dock, 3D formats. ZINC also contains over 750 million purchasable compounds that can be searched for analogs.

This Dataset contains year and district wise received, unusable and distributed stock data for all the indicators related to Family Planning, Maternal Health, and Immunization under HMIS for Kerala

Predicted druglikeness and ADMET analysis of the compounds.

This Dataset contains year and district wise received, unusable and distributed stock data for all the indicators related to Family Planning, Maternal Health, and Immunization under HMIS for Andaman and Nicobar Islands

The table includes 251 products with the active ingredient Zinc.

Chemical libraries have become of utmost importance to boost drug discovery processes. It is widely accepted that the quality of a chemical library depends, among others, on its availability and chemical diversity which help in rising the chances of finding good hits. In this regard, our group has developed a source for useful chemicals named Medicinal and Biological Chemistry (MBC) library. It originates from more than 30 years of experience in drug design and discovery of our research group and has successfully provided effective hits for neurological, neurodegenerative and infectious diseases. Moreover, in the last years, the European research infrastructure for chemical biology EU-OPENSCREEN has generated the European Chemical Biology library (ECBL) to be used as a source of hits for drug discovery. Here we present and discuss the updated version of the MBC library (MBC v.2022), enriched with new scaffolds and containing more than 2,500 compounds together with ECBL that collects about 100,000 small molecules. To properly address the improved potentialities of the new version of our MBC library in drug discovery, up to 44 among physicochemical and pharmaceutical properties have been calculated and compared with those of other well-known publicly available libraries. For comparison, we have used ZINC20, DrugBank, ChEMBL library, ECBL and NuBBE along with an approved drug library. Final results allowed to confirm the competitive chemical space covered by MBC v.2022 and ECBL together with suitable drug-like properties. In all, we can affirm that these two libraries represent an interesting source of new hits for drug discovery.

Anti-TB activity prediction of top drugs through online server mycoCSM.

This Dataset contains month and district wise received, unusable and distributed stock data for all the indicators related to Family Planning, Maternal Health, and Immunization under HMIS for Uttar Pradesh

Datasets used as input, generated or analyzed in the manuscript De Novo Design of κ-Opioid Receptor Antagonists Using a Generative Deep Learning Framework:

1. Pre-training Dataset #1 (4.6 million compounds from ZINC 20): zinc20_train.csv (494 MB)
2. Pre-training test set (0.5 million compounds from ZINC 20): zinc20_test.csv (55 MB)
3. Pre-training test scaffolds set (0.5 million compounds from ZINC 20): zinc20_test_scaffolds.csv (57 MB)
4. Pre-training Dataset #2 (2,056 compounds from ChEMBL with IC50 data): chembl30_kor_inhibitors.csv (144 KB)
5. 169 compounds prioritized for chemical synthesis on the basis of their similarity to JDTic's interactions with KOR: prioritized_169candidates_from1M_by_SIFt_Tc.csv (17 KB)

All datasets are in Simplified Molecular Input Line Entry System (SMILES) format.

The value is based on the mean grain zinc value in the cultivars which are common across the five trials.

This Dataset contains month and district wise received, unusable and distributed stock data for all the indicators related to Family Planning, Maternal Health, and Immunization under HMIS for Daman and Diu

The table includes 356 products with the active ingredient Bacitracin Zinc.

This Dataset contains year and district wise received, unusable and distributed stock data for all the indicators related to Family Planning, Maternal Health, and Immunization under HMIS for Himachal Pradesh

This Dataset contains year and district wise received, unusable and distributed stock data for all the indicators related to Family Planning, Maternal Health, and Immunization under HMIS for Lakshadweep

This Dataset contains month and district wise received, unusable and distributed stock data for all the indicators related to Family Planning, Maternal Health, and Immunization under HMIS for Madhya Pradesh

This Dataset contains year and district wise received, unusable and distributed stock data for all the indicators related to Family Planning, Maternal Health, and Immunization under HMIS for Delhi