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About Schrodinger

Schrodinger (NASDAQ: SDGR) operates as a physics-based computational platform for drug discovery and materials research. The company's main offerings include a digital chemistry platform that uses physics and machine learning to facilitate molecular design, as well as software solutions for small molecule drug discovery, biologics drug discovery, and materials science. Schrodinger primarily serves the pharmaceutical and biotechnology industries, as well as the materials research sector. The company was founded in 1990 and is based in New York, New York.

Headquarters Location

1540 Broadway 24th Floor

New York, New York, 10036,

United States




Research containing Schrodinger

Get data-driven expert analysis from the CB Insights Intelligence Unit.

CB Insights Intelligence Analysts have mentioned Schrodinger in 1 CB Insights research brief, most recently on Nov 1, 2023.

Expert Collections containing Schrodinger

Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.

Schrodinger is included in 2 Expert Collections, including Digital Health.


Digital Health

10,585 items

The digital health collection includes vendors developing software, platforms, sensor & robotic hardware, health data infrastructure, and tech-enabled services in healthcare. The list excludes pureplay pharma/biopharma, sequencing instruments, gene editing, and assistive tech.


Drug Discovery Tech Market Map

221 items

This CB Insights Tech Market Map highlights 220 drug discovery companies that are addressing 12 distinct technology priorities that pharmaceutical companies face.

Schrodinger Patents

Schrodinger has filed 7 patents.

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Computational chemistry, Statistical mechanics, Drug discovery, Physical chemistry, Thermodynamics


Application Date


Grant Date



Related Topics

Computational chemistry, Statistical mechanics, Drug discovery, Physical chemistry, Thermodynamics



Latest Schrodinger News

Structure-based identification of novel inhibitors targeting the enoyl-ACP reductase enzyme of Acinetobacter baumannii

Dec 4, 2023

Abstract Acinetobacter baumannii is a Gram-negative multidrug-resistant bacterial pathogen primarily associated with nosocomial infections resulting in increased morbidity and mortality in adults and infants, especially in sub-Saharan Africa where the clinical burden is high. New therapeutics are needed to treat multidrug-resistant Acinetobacter baumannii infections and reduce transmission. The study used computer-integrated drug discovery approaches including pharmacophore modelling, molecular docking, and molecular dynamics simulation to screen potential inhibitors against the enoyl-acyl carrier protein reductase—FabI protein of Acinetobacter baumannii. The top three potential inhibitors: 21272541 > 89795992 > 89792657 showed favourable binding free energies including coulombic energy, van der Waals energy, and polar and non-polar energies. Furthermore, all three complexes were extremely stable and compact with reduced fluctuations during the simulations period. Inhibitor 21272541 exhibited the highest binding affinity against the Acinetobacter baumannii FabI protein. This is similar to our recent report, which also identified 21272541 as the lead inhibitor against Klebsiella pneumoniae infections. Future clinical studies evaluating drug effectiveness should prioritise inhibitor 21272541 which could be effective in treating infections caused by Gram-negative organisms. Introduction Acinetobacter baumannii (A. baumannii) is a Gram-negative opportunistic organism associated with nosocomial bloodstream, urinary tract and airways infections, particularly in immunocompromised individuals and in intensive care units (ICU) facilities. Globally, A. baumannii is responsible for 1.27 million annual deaths, with the burden of disease being highest in Sub-Saharan Africa (27·3 deaths per 100 000) 1 where there is limited access to healthcare resources. The World Health Organization (WHO) has identified A. baumannii as a multidrug-resistance (MDR) critical priority pathogen as it causes deadly bloodstream infections 2 , which in the absence of preventive measures such improved infection control and development of vaccines or new antimicrobial drugs, could potentially contribute to 10 million deaths annually by 2050. A. baumannii forms a part of the ESAKPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) organisms which are associated with infections acquired in hospitals (HAI); and are characterised by high levels of resistance towards multiple classes of antibiotics leading to reduced therapeutic options 3 . Antimicrobial resistance has spread globally through various mechanisms including extended-spectrum β-lactamases (ESBLs), carbapenemases and AmpC beta-lactamases enzymes (AmpC) 4 . The high prevalence of both extreme drug resistant (XDR) and pan drug resistant (PDR) 5 A. baumannii strains poses a challenge for clinical therapeutics. Furthermore, MDR A. baumannii is often associated with inappropriate empiric therapies which contribute to high case fatality rates 6 . Acinetobacter baumannii isolates manifesting XDR are defined as being highly resistant to three or more classes of antibiotics, including fluoroquinolones, penicillins, cephalosporins with inhibitor combinations and aminoglycosides. Resistance of A. baumannii isolates to carbapenems (i.e., PDR) is associated to polymyxins and tigecycline antimicrobial class of drugs 7 . There is a paucity of data on invasive A. baumannii disease cases due to PDR strains, which is associated with severe illness, multi-morbidity, long hospital admissions and exposure to multi-invasive procedures 8 . Although some developments of new antibiotics targeting Gram-negative bacteria are currently underway, additional antimicrobial therapy options are required as spread of MDR, XDR and PDR A. baumannii strains continues to emerge as a major health concern 9 . All Gram-negative bacteria have a distinctive outer membrane composed of glycolipid lipopolysaccharides and glycerol phospholipids that acts as a protective barrier against antibiotics or other toxic compounds 10 . Fatty acids can be formed through two different pathways containing type I and type Fatty Acid Synthesis (FASI and FASII). FASI is achieved by an enzyme using a large multidomain and multifunctional structure in mammals whereas FASII pathway utilized by bacteria involving chains of distinct and functionally specific enzymes producing fatty acids which ordained to integrate in membrane lipids. Acyl-carrier protein (ACP) is attached to the acyl chain through a thioester linkage and accountable to transport the substrate among enzymes of the FASII pathway. The pathway then initiates with the condensation of acetyl-CoA and malonyl-ACP via Fatty acid biosynthesis enzyme-FabH. This step is followed by eliminating β-keto group by FabG, dehydration of the substrate by FabZ or FabA, and then reduction by FabI, FabL, FabK, or FabV. Condensation by FabB or FabF commences a new cycle which repeats, elongating the fatty acid chain by two carbons each cycle. Hence, novel antimicrobial drugs should be designed targeting non-homologous bacterial enzymes due to differences in FASI and FASII pathways. Bacteria’s can shelter several FabI isoforms (viz. FabI/FabL/FabK/FabV), however A. baumannii only contains a single FabI enzyme 11 involved in FAS-II 12 present in the cytoplasmic membrane. Therefore, new therapeutics should be focused on inhibiting the biological targets located in the cytoplasmic membrane of Gram-negative organisms. The NADH dependent FabI catalyses the last reaction in FAS-II by converting trans-2-enoyl-ACP to acyl-ACP and removing the double bond at second position of a fatty acid chain 13 . The enzyme also involved in the fatty acid elongation cycle, which is essential in lipid metabolism and biotin biosynthesis 14 . The role of FabI in catalysing FAS-II in A. baumannii makes it a favourable target for the discovery and development of novel antimicrobial agents 15 . Triclosan is a widely used antimicrobial agent and blocks the activity of bacterial FAS-II 16 , hence, we have used A. baumannii FabI protein in complex with triclosan and NADH as a model system to design A. baumannii specific inhibitors. This study focuses the advancement of novel antimicrobial agents targeted at the A. baumannii FabI protein. Computational methodologies Protein structure preparation The structure of the A. baumannii FabI protein in complex with triclosan and NADH was retrieved from the Protein Data Bank (PDB) database 17 under the accession number of 6AH9 ( ) and used as a modelling system to perform molecular docking and extended molecular dynamics simulations studies. The structure of the protein was processed, minimized and taken for refinement using Protein Preparation Wizard 18 in Maestro 13.3 version of Schrodinger package. The charge and protonation state were equivalent to pH 7.0 on the protein structure considering all basic and acidic amino acids. Afterwards, energy minimization was applied with the liquid simulations force field (OPLS-2005) on protein’s structure with root mean square deviation (RMSD) cut-off 0.30 Å to remove any steric clashes from the residues after adding hydrogen atoms and hetero-groups. Ligand structure preparation Triclosan was used as a model compound to screen a library of available compounds from PubChem database 19 . PubChem is a public repository for chemical compounds with their biological activities as this information lacks in other databases like ZINC. This database is also integrated in other biomedical database hosted by National Institutes of Health (NIH) including PubMed, Protein, Gene, BioAssay etc. making it more relevant in comparison with other publicly available databases 20 . The ligand-dataset of 140 compounds were filtered based on a 90% similarity index including Lipinski’s rule of Five 21 from the database. This virtual library was then taken for comprehensive screening evaluations. All chemical structures including triclosan were prepared using the LigPrep module 22 of the Schrodinger package involving addition of hydrogen atoms, selecting correct orientation, ring conformations and ionization states. The liquid simulations force field (OPLS-2005) was used to apply partial charges on the compounds and the compounds were then exposed to energy minimization until RMSD reached to 0.001 Å. Epik ionization 23 tool was used to neutralize the pH of all compounds. Identification of pharmacophore hypotheses The structure-based e-pharmacophore strategic approach was considered using the PHASE 24 modules of Schrodinger package. Hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic contacts (H), negative ionizable (N), positive ionizable (P), and aromatic ring (R) were selected as default chemical features to generate the most illustrative features of the active site of A. baumannii FabI protein. Hypothesis Generation for Energy-Optimized Structure Based Pharmacophores (e-Pharmacophore) was used to process the features with excluded 5 Å volume of the refined inhibitor for the FabI protein. Pharmacophore features were then selected based on the critical interactions with the key amino acids of protein involved around the pharmacophore inhibitor. The generated pharmacophore characteristics contained all functional groups to inhibit the activity A. baumannii FabI protein. Screening of the A. baumannii FabI inhibitor-like compounds using docking-based virtual screening To screen the library of 140 compounds extracted from the PubChem database against A. baumannii FabI protein, the obtained 3D pharmacophore features were exported and established as a reference for the PHASE-based virtual screening protocol to identify potential compound against FabI protein. A total of 136 (97%) out of 140 compounds were selected for further analyses based on their highest negative PHASE scores and the identical attributes of the compounds were also considered using the Phase Screen Scores. The Glide-based virtual screening module 25 of Schrodinger package was used to filter the lead compounds that strongly interacted by producing multiple number of non-covalent interactions with the A. baumannii FabI protein. The grid around the active site of receptor was built with a big cubical box (28 × 28 × 28 Å) and a small cubical box (20 × 20 × 20 Å) for accurate binding. The library was then docked in a three-step docking protocol with High throughput Virtual Screening (HTVS) followed by Standard Precision (SP) and then Extra-Precision (XP) modes 26 , 27 . The Docking-Based Virtual Screening (DBVS) protocol was used to filter all 136 compounds. Twenty-three (32%) were chosen for further analyses based on the XP-GScore. The docked complexes were ranked based on their binding scores and best interaction poses. The stability of these complexes assumed to have higher potential as their binding energies were having negative scores. Ligand-based ADME/toxicity properties assessment The Absorption, Distribution, Metabolism, Excretion and or Toxicity (ADME/T) analysis on the 23 filtered compounds was performed using QikProp 5.6 module 28 in the Schrodinger package to generate the related descriptors. Briefly, QikProp assess significant descriptors based on pharmacokinetic and physiochemical properties of the compounds. The ADME/T analysis includes membrane permeability (log P), blood–brain barrier (log BB), central nervous system (CNS), solvent surface accessible area (SASA), solubility (log S) and percentage of human oral absorption of the compounds. All these factors are important to filter a potential compound to be an ideal drug. The acceptable range for CNS is from–2 (inactive) to + 2 (active), for SASA it should be in a range from 300 to1000, for octanol/water partition coefficient the standard acceptable range is between 2 to 6.5, for aqueous solubility the normal range is − 6.5 to 0.5 mol/dm, for BB the drug candidate should be fall between − 3.0 and 1.2 and the percentage of human oral absorption must be < 25% and > 80%. The processed ADME/T properties 27 define the drug-likeness activity of the filtered compounds leads to the selection of top three hit compounds: 21272541, 89795992, 89792657 which further directed towards the Molecular dynamic (MD) simulations for extended analysis. Molecular dynamic (MD) simulation studies The elaborated method for MD simulation has already been described in our recently published report 29 . Briefly, the Assisted Model Building with Energy Refinement (AMBER) 18 tool 30 was used to perform 200 ns of MD simulations on the four complexes: 21272541-FabI, 89795992-FabI, 89792657-FabI with triclosan-FabI as a model system. Graphics Processing Unit (GPU) accelerated version of Partial Mesh Ewald Molecular Dynamics (PMEMD) was used for fast and accurate simulations 31 . The general AMBER force field (GAFF) 32 ff14SB was used for ligand–protein parametrization and inbuilt parameters for NADH co-factor was used with the LEaP module 32 of AMBER 18. Antechamber module with GAFF force field was used to assign the partial charges and to generate the atom types for each molecule. Furthermore, the LEaP module was used to generate the TIP3P water box of 10 Å, neutralize the systems with sodium and chloride ions with neutral charge, and to create the topology and input coordinate files for simulation studies. All four systems were then taken for 200 ns MD simulations to analyse the behaviour of our chosen compounds against A. baumannii FabI drug discovery. Post-dynamic trajectory analysis After the completion of the MD simulation, trajectories were analysed with CPPTRAJ module 33 implemented in AMBER 18 tools. It is the main program in AMBER for processing coordinate trajectories to perform complicated analysis. The root mean square deviation (RMSD) of Cα atoms, root mean square fluctuation (RMSF) of amino acid residues in the complex, radius of gyration (RoG), solvent accessible surface area (SASA), intra-molecular hydrogen bond, distance correlation matrix and principal component analysis was performed with this module. For scheming the 2-dimensional (2D) plots of the trajectories, Origin 7.0 graphic and data analysing tool 34 was used. Binding free energy calculation and per-residue free energy decomposition Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) 35 is a widely used technique to calculate free energies of the bound ligand to its associated protein/s. CPPTRAJ module was used to strip all water molecules, ions and solvents from the trajectories. The free energies (ΔGbind) were computed using below equation for all four systems: $$\Delta G_{bind} = \, G_{complex} {-}G_{protein} {-}G_{ligand}$$ (1)

Schrodinger Frequently Asked Questions (FAQ)

  • When was Schrodinger founded?

    Schrodinger was founded in 1990.

  • Where is Schrodinger's headquarters?

    Schrodinger's headquarters is located at 1540 Broadway, New York.

  • What is Schrodinger's latest funding round?

    Schrodinger's latest funding round is IPO.

  • How much did Schrodinger raise?

    Schrodinger raised a total of $162M.

  • Who are the investors of Schrodinger?

    Investors of Schrodinger include Ono Pharmaceuticals, Sanofi, Pavilion Capital, Michael Antonov, Laurion Capital Management and 9 more.

  • Who are Schrodinger's competitors?

    Competitors of Schrodinger include Cradle, POLARISqb, Citrine Informatics, Collaborations Pharmaceuticals, DP Technology and 7 more.


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