SeeSAR SeeSAR
down

SeeSAR

The Liberating Drug Design Experience
fast

fast

Dock, design, and analyze your compound in a flash, with swift and informative calculations.
visual

visual

Evaluate ligand-target interactions by intuitive color codes and gorgeous visualization.
easy

easy

We provide a satisfying on-the-fly drug design experience. No learning curve!
What's inside?
protein mode to search and load your protein

Protein Mode

Drag and drop your protein, or search comfortably in an online database. Within seconds, your target is prepared and you can get started.
protein editor mode

Protein Editor Mode

Modify your protein according to your needs. Explore rotamers, introduce mutations, and customize side chains.
binding site mode for target pocket detection

Binding Site Mode

SeeSAR automatically detects the binding site of a ligand for you. In addition, you can precisely expand it by adding individual residues — or with a single click to find empty pockets in your protein.
molecule editor mode for on-the-fly modifications of your compounds

Molecule Editor Mode

Modify molecules to your taste in 2D or 3D on-the-fly. Once you are done, the molecules are directly prepared for your tasks.
analyzer mode for property assessment

Analyzer Mode

Estimate affinities and interpret the results using the visualized HYDE score. Filter your compounds for relevant parameters, calculate ADME properties, and gain full control over ligand-target interactions.
inspirator mode for interesting new structural proposals

Inspirator Mode

Ideate without limits! Discover new scaffolds, explore, and grow into free cavities, or link molecules using fragment libraries for elegant solutions.
docking mode for binding mode predictions

Docking Mode

Dock your compounds with one single click! Screen libraries for actives, and instinctively evaluate your results.

Similarity Scanner Mode

Align your compounds without the need of a target structure based on their molecular similarity.

Universal toolkit

SeeSAR fosters innovation during every step of your drug design process. The app includes all tools vital for handling your compounds and target structures which have been fine-tuned to the needs of any chemist.
Helpful features such as ADME properties assessment, comprehensive color coding, unoccupied binding pocket visualization, and many others, support you in making sound and interactive decisions.
All our tools are based on solid and transparent science cited in over a thousand publications. Follow the button if you want to learn more about the science behind SeeSAR.
Science

What others say

This software was incredibly useful in the running of the coursework exercise in our final year units as SeeSAR was really easy to use for my students.
—
Dr. Stephen Flower, University of Bath, UK
SeeSAR is easy to use, and allows scientists from different disciplines to explore new design ideas.
—
Dr. Sharon Shechter, Silicon Therapeutics, USA
Its super-duper fast! Much much faster than any pharmacophore screening I've ever done before!
—
Dr. Jessica Holien, RMIT Melbourne, Australia
We are heavy SeeSAR users and our students, undergraduate and postgraduate alike, absolutely love it. Numbers of students interested in computational chemistry increased since we have introduced SeeSAR.
—
Dr. Agnieszka K. Bronowska, Newcastle University, UK
SeeSAR is by far the best idea generator in Medicinal Chemistry.
—
Dr. Peter Sennhenn, transMedChem, Germany
SeeSAR allowed us to understand a specific halogen substitution pattern crucial for robust activity in our functional assays.
—
Dr. Sander Nabuurs, LeadPharma, The Netherlands
The seamless integration between StarDrop and SeeSAR provides a state-of-the-art drug design and development platform to our very unique high school program.
—
Robert R. Gotwals, the North Carolina School of Science and Mathematics, Durham, USA
HYDE really gave this improvement!
—
Dr. Hans Briem, BAYER
Previous Next

Five reasons for SeeSAR

Fragment files

Ultra-fast pocket exploration

SeeSAR provides users with a powerful tool to rapidly screen for ligand decorations or extensions that explore or fill binding cavities of a target structure. The ultra-fast component behind this method, FastGrow, uses a novel and highly efficient algorithm with shape-based directional descriptors to screen hundred-thousands of fragments within seconds on standard hardware to create optimized suggestions.

A library of 12,000 fragments is featured per default in SeeSAR. A large, more diverse library is available for download:

Google-like re-scaffolding

SeeSAR helps you to generate new intellectual property or get rid of issues in molecules. Supported by the ReCore tool implemented in our Inspirator mode, SeeSAR searches in pre-processed libraries, so-called “indices”, for fitting replacements in selected molecule areas. Our approach to fragment-based lead discovery (FBLD) delivers suggestions to core replacements, molecule growing, and fragment linking within seconds.
BioSolveIT offers the required index files for free.
* The CSD SeeSAR index requires a license from CCDC.

Covalent Supplier Libraries

A covalent binding mode may have many advantages — including improved selectivity and prolonged pharmacological duration. With the recent introduction of covalent docking into SeeSAR, you now have everything at hand to explore and discover covalent binders at any suitable residue in your binding site. In a collaborative effort, we have translated several compound supplier libraries into a convenient, ready-to-dock SeeSAR format. Thus, over 30 warheads can be covalently docked at your target of interest and assessed for their binding mode.

Important note: Due to the current limitation of 50,000 entries in SeeSAR tables, we recommend a pre-filtering of the big libraries (e.g. with KNIME®). We offer a "Teaser Set" of 10k randomly selected compounds with warheads targeting CYS residues. Any library can be filtered by target residue, warhead, MW, and much more.
You can use this to test and playfully explore the covalent docking feature of SeeSAR together with the Covalent Docking Guide (PDF) on your own.

For support in setting up your library or virtual screening please do not hesitate to contact us.

The libraries can be accessed here:
Updates

SeeSAR Version 12.1 — Narcissus

SeeSAR 'Narcissus' was apparelled with a novel, powerful tool for lightning-fast pocket exploration: FastGrow. Fastgrow uses a novel, fast, and accurate shape-based algorithm for structure-based growing into binding pockets. Users can screen tens of thousands of fragments within seconds to receive an optimized suggestion that complements the binding site. Application scenarios of FastGrow involve fragment-based growing approaches, group replacement to improve affinity, scaffold hopping towards particular binding motives of interest (e.g., a hinge binding motif), screening of pocket filling groups to increase selectivity, compound library generation, and many more.
Further, we added several other augmentations to provide users with more options to guide the outcomes of results (e.g. handling of six-membered rings).

For older versions and an elaborated list please visit here

Help
Beginner's Guide (PDF) First Aid

60 seconds videos


Tutorial videos

Advanced Guides

FAQ
SeeSAR can calculate and predict following parameters of a molecule that can be further used for filtering steps and compound assessment: HYDE-based (Lipophilic) ligand efficacy (LE/LLE), molecular weight, logP, total polar surface area (TPSA) of a compound, H-bonds, H-bond acceptors and donators, heavy atoms, aromatic rings, maximum ring size, total charge, and presence of covalent warheads. You can also calculcate and filter for following numbers: odd torsions, heavy atoms, (aromatic) rings, aromatic atoms, nitrogen and oxygen atoms, halogens, stereo centers, stereo bonds, and rotatble bonds. With this you can easily tailor your filters for particular compound features and
Furthermore, SeeSAR supports the Optribrium expansion to predict a variety of important ADME parameters for further compound assessment:
ADME parameter
2C9 pKi Cytochrome P450 CYP2C9 pKi prediction. Affinity prediction of the compound to bind at the enzyme involved in several metabolic drug pathways.
2D6 affinity category Cytochrome P450 CYP2D6 classification. Compounds are predicted to be in one of four categories: ‘low’ for compounds with a pKi<5, ‘medium for compounds with a pKi between 5 and 6, ‘high for compounds with a pKi between 6 and 7, and ‘very high’ for compounds with a pKi>7.
BBB category Classification of compounds into ‘+’-category if log([brain]:[blood])≥-0.5 and ‘-’category if log([brain]:[blood])<-0.5.
BBB log([brain]:[blood]) Logarithm of brain-blood partition coefficient of a compound. Can be used as indicator for CNS active compounds.
HIA category Human intestinal absorption classification. Compound which are predicted to be absorbed ≥30% are classified with ‘+’, compounds which are predicted to be absorbed <30% are classified with ‘-’.
P-gp category P-glycoprotein 1 (also known as multidrug resistance protein 1 (MDR1) and ATP-binding cassette sub-family B member 1 (ABCB1)) transport classification. Predicts if a compound is a substrate of P-gp.
PPB90 category Plasma protein binding classification. Predicts a classification of ‘low’ for compounds which are <90% bound and high for compounds which are >90% bound.
hERG pIC50 Prediction of pIC50 values for inhibition of human Ether-a-go-go Related Gene (hERG) potassium channels expressed in mammalian cells.
logD Logarithm of n-octanol-water partition coefficient (also known as n-octanol-water partition ratio) at fixed physiological pH 7.4. Used to describe the relationship between lipophilicity and hydrophilicity of an ionized compound.
logP Logarithm of n-octanol-water partition coefficient. Used to describe the relationship between lipophilicity and hydrophilicity of a neutral compound.
logS Logarithm of intrinsic aqueous solubility in µM for neutral compounds.
logS @ pH 7.4 Logarithm of intrinsic aqueous solubility at physiological pH 7.4 in µM for ionized compounds.
Any usage with a graphical user interface requires a local installation and a local graphics card. This can therefore not work from remote.
Commandline and KNIME runs however can be triggered from remote.
Please contact us if you need to work from home during the Corona pandemic.

Couldn’t find what you are looking for?
Visit our elaborative FAQ section or the first aid section

FAQ First Aid
Science

HYDE - Interactive, desolvation-aware visual ΔG estimates

HYDE binding assessment approximates and visualizes affinities. The system has NOT been trained to specific targets, instead implicit HYdrogen bond and DEhydration are intrinsically balanced without weighting parameters as seen in all force fields. The user instantly gets interpretative feedback for lead optimization and other design tasks.
HYDE is constantly improved and originated from a collaboration with BAYER, Hamburg University, and BioSolveIT.

Find out more about HYDE here (PDF).

ReCore - 3D scaffold hopping

Replace a given core and generate new intellectual property. You can specify bonds or interactions to be matched by new fragments. The arrangement of the connected residues is taken to a fragment library that has been pre-processed for speed (“indexed”). Results are retrieved using a 4-dimensional vector and the quality of the fit is computed. Indices can be custom designed with in-house compound libraries.
ReCore emerged from a collaboration with Roche Basel and Hamburg University and has been extensively augmented and extended by BioSolveIT thereafter.

Find out more about ReCore here (PDF).

Visual torsions - Statistical assessment of likelihood of dihedrals

Based on rigorous statistical analysis of small molecules in crystal structure databases, the “traffic-light” implementation for the torsion angles in molecules reflects the frequency of occurrence of a given dihedral. The underlying assumption is that frequent observations correlate with low energy structures and vice versa.
The Visual Torsions emerged from a collaboration with Roche Basel and Hamburg University.

Publications on visual torsions can be found here and here.

Pocket detection - Druggable binding sites from 3D structures

Compute proposals for accessible empty pockets, and visualize the results in 3D for further selection. The functionality is based on a heuristic model that employs Gaussian differences on a 3D grid to assess the likelihood of dealing with a pocket shape or not. Global hydrogen bond functionalities and the lipophilic character - plus the solvent accessible surface (SAS) of the putative pocket are taken into account. The computation is further enriched with local measures such as distances between pairs of functional group atoms.
The pocket detection algorithms are part of the DoGSiteScorer that emerged from a collaboration with Merck and Hamburg University.

Publications on our DoGSiteScorer-based pocket detection can be found here and here.

FlexX docking - Fast, flexible placement of ligands into cavities

Dock a ligand into a receptor cavity. This state-of-the-art algorithm splits ligands into so-called fragments which are placed into multiple places in the pocket – and scored using a simple, yet very fast pre-scoring scheme. From the n solutions placed, the ligand is further built up, fragment by fragment, and the interim solutions are scored against each other. The best scored survive the process, and those are delivered to the user. The “Single Interaction Scan” (SIS) placement also finds solutions when there are only very few polar groups in a compound.

Find out more about FlexX here (PDF).

FlexS — Ligand-based similarity search

FlexS is a computer program for predicting ligand alignments. For a given pair of ligands, FlexS predicts the conformation and orientation of one of the ligands relative to the other one. Without the need of a target 3D structure users can use FlexS as part of the Similarity Scanner Mode to perform virtual screening on molecule sets to discover similar compounds to a reference ligand. FlexS takes into account the shape and molecular features of the molecule and can be used for scaffold hopping and generation of binding mode hypothesis.

Find out more about FlexS here (PDF).

FastGrow — Lightning-Fast Pocket Exploration

FastGrow enables users to breathtakingly fast search for ligand decorations or extensions that complement unoccupied binding cavities of a target structure. This novel tool enables interactively explorative growing and put control over the growing process firmly back into the user’s hand. Further, users can apply pharmacophore constraints to fine-tune their search according to their needs.
Being developed at Hamburg University in a joint collaboration with Servier Paris and BioSolveIT, FastGrow has already partied first successes at AbbVie. It has been thoroughly validated on real fragment growings/replacements in various scenarios.

Find out more about FastGrow here (PDF).

Optibrium - ADME properties prediction

Physicochemical properties are an important factor to consider to decide if a compound is promising drug candidate. Early assessment of ADME parameters can predict toxicity issues and absorption challenges in the early stages of drug discovery saving time and ressources. While SeeSAR can calculate a variety of interesting filter parameters (e.g. rotable bonds, molecular weight, TPSA), it also supports ADME parameter predictions by the optional StarDrop module by Optibrium. With this users have the possibility to calculate important parameters like CYP enzyme affinity, blood brain barrier to blood distribution, logS, and many more.

Find out more about Optibrium ADME properties here (PDF).

Recent success stories

Application of FastGrow for fragment growing in a case study on the DYRK1A kinase.
FastGrow: on-the-fly growing and its application to DYRK1A.
Penner, P.; Martiny, V.; Bellmann, L.; Flachsenberg, F.; Gastreich, M.; Theret, I.; Meyer, C.; Rarey, M.
Journal of Computer-Aided Molecular Design 2022.
A virtual screening campaign at two targets, namely FLT3 and MNK2, resulted in the discovery of a nanomolar, small molecule dual inhibitor. FlexX and Hyde were successfully used on hit compounds and close analogs to elucidate SAR. The most promising candidate was selective versus 82 other kinases.
Identification of a Dual FLT3 and MNK2 Inhibitor for Acute Myeloid Leukemia Treatment Using a Structure-Based Virtual Screening Approach.
Yen, S.-C.; Chen, L.-C.; Huang, H.-L.; HuangFu, W.-C.; Chen, Y.-Y.; Eight Lin, T.; Lien, S.-T.; Tseng, H.-J.; Sung, T.-Y.; Hsieh, J.-H.; Huang, W.-J.; Pan, S.-L.; Hsu, K.-C.
Bioorg. Chem. 2022, 121 (February), 105675.
Taking a rather unusual route, namely decreasing the affinity of a compound, Kulkarni et al. used SeeSAR to novel scaffolds of the xenoestrogen bisphenol A (BPA) with an improved pharmacological profile in the human endocrine system. ReCore was used to screen for replacements of the hydrophobic core important for binding at the estrogen receptor. SeeSARs visualization features connected in silico modelling to the in vitro results.
Estrogenic Activity of Tetrazole Derivatives Bearing Bisphenol Structures: Computational Studies, Synthesis, and In Vitro Assessment.
Gadgoli, U. B.; Y.C., S. K.; Kumar, D.; Pai, M. M.; Pulya, S.; Ghosh, B.; Kulkarni, O. P.
J. Chem. Inf. Model. 2022, No. 1
Researchers from the University of Bonn and BioSolveIT use a two-pronged experimental and computational approach in the discovery of linear and cyclic lead peptides with potential for modulating nucleotide exchange on "undruggable" G proteins. SeeSAR has been used in all phases of the study, from the identification of binding sites, comparison of binding sites, and finally HYDE-visualizations immensely aid in tying the outcomes together.
Targeting Gαi/s Proteins with Peptidyl Nucleotide Exchange Modulators.
Nubbemeyer, B.; Paul George, A. A.; Kühl, T.; Pepanian, A.; Beck, M. S.; Maghraby, R.; Shetab Boushehri, M.; Muehlhaupt, M.; Pfeil, E. M.; Annala, S. K.; Ammer, H.; Imhof, D.; Pei, D.
ACS Chem. Biol. 2022.
Discovery of novel natural products as binders at an allosteric binding site by a hybrid docking workflow. The group was able to discover four novel compounds for a difficult target and an even more difficult binding site with FlexX where a conventional high-throughput screening of 400k compounds resulted in only one lead compound.
Decrypting a Cryptic Allosteric Pocket in H. Pylori Glutamate Racemase.
Chheda, P. R.; Cooling, G. T.; Dean, S. F.; Propp, J.; Hobbs, K. F.; Spies, M. A.
Commun. Chem. 2021, 4 (1), 172.
Development of novel drugs for Alzheimer's Disease by Zaib et al. via a pharmacophore hybridization strategy to design and exlore the wider chemical space for new and potent cholinesterase inhibitors. Docking predictions and ligand design resulted in an highly active compound (IC50 = 0.12 µM).
Hybrid Quinoline-Thiosemicarbazone Therapeutics as a New Treatment Opportunity for Alzheimer’s Disease-Synthesis, In Vitro Cholinesterase Inhibitory Potential and Computational Modeling Analysis.
Zaib, S.; Munir, R.; Younas, M. T.; Kausar, N.; Ibrar, A.; Aqsa, S.; Shahid, N.; Asif, T. T.; Alsaab, H. O.; Khan, I.
Molecules 2021, 21, 6573.
Report on an iterative cycle of design, synthesis and biological evaluation of dual BET and HDAC inhibitors by the Günther and Hansen groups. Using SeeSAR, complex structures of BRD4 with inhibitors were extended and modified and docking poses were assessed with HYDE for compound optimization.
4-Acyl Pyrrole Capped HDAC Inhibitors: A New Scaffold for Hybrid Inhibitors of BET Proteins and Histone Deacetylases as Antileukemia Drug Leads.
Schäker-Hübner, L.; Warstat, R.; Ahlert, H.; Mishra, P.; Kraft, F. B.; Schliehe-Diecks, J.; Schöler, A.; Borkhardt, A.; Breit, B.; Bhatia, S.; Hügle, M.; Günther, S.; Hansen, F.
J. Med. Chem. 2021, 64 (19), 14620–14646.
Targeting a prion folding intermediate, Biasini et al. performed a virtual screening campaign with the goal to discover modulators for the predicted folding pathway. Four compounds were discovered and confirmed in vitro as hits thus paving the way for a complete new mechanism of action.
Pharmacological Inactivation of the Prion Protein by Targeting a Folding Intermediate.
Spagnolli, G.; Massignan, T.; Astol, A.; Biggi, S.; Rigoli, M.; Brunelli, P.; Libergoli, M.; Ianeselli, A.; Orioli, S.; Boldrini, A.; Terruzzi, L.; Bonaldo, V.; Maietta, G.; Lorenzo, N. L.; Fernandez, L. C.; Codeseira, Y. B.; Tosatto, L.; Linsenmeier, L.; Vignoli, B.; Petris, G.; Gasparotto, D.; Pennuto, M.; Guella, G.; Canossa, M.; Altmeppen, H. C.; Lolli, G.; Biressi, S.; Pastor, M. M.; Requena, J. R.; Mancini, I.; Barreca, M. L.; Faccioli, P.; Biasini, E.
Nat. Comm. Biol. 2021, 4 (62), 1–16.
Cocklin et al. describe their computational workflow to design novel HIV-1 capsid inhibitors with improved metabolic stability. In only three analog steps they were able to increase the half time 200-fold in comparison to the starting compound.
Rapid Optimization of the Metabolic Stability of a Human Immuno Deficiency Virus Type ‑ 1 Capsid Inhibitor Using a Multistep Computational Workflow.
Meuser, M. E.; Adi, P.; Reddy, N.; Dick, A.; Maurancy, J. M.; Salvino, J. M.; Cocklin, S.
J. Med. Chem. 2021, 64 (7), 3747–376.
In this publication Günther et al. report on the design of novel dual BET-BRD7/9 bromodomain inhibitors containing a 4-acyl pyrrol moiety. Molecular docking studies with SeeSAR were performed to predict the binding mode of the nanomolar compound 11 and elucidate the beneficial effect of an oxygen atom in the terminal group.
4 ‑ Acyl Pyrroles as Dual BET-BRD7 / 9 Bromodomain Inhibitors Address BETi Insensitive Human Cancer Cell Lines.
Hu, M.; Regenass, P.; Warstat, R.; Hau, M.; Schmidtkunz, K.; Lucas, X.; Wohlwend, D.; Einsle, O.; Jung, M.; Breit, B.; Günther, S.
J. Med. Chem. 2020, 63 (24), 15603–15620.

Get SeeSAR and start your drug discovery project!

Download

How to cite

In publications please cite SeeSAR with the respective version number as follows:
SeeSAR version 12.1.0; BioSolveIT GmbH, Sankt Augustin, Germany, 2022, www.biosolveit.de/SeeSAR