SeeSAR

SeeSAR incorporates a set of 12k medchem-like growing fragments right from the start.
Simply load your molecule into the Inspirator Mode and select the part you would like to grow from or a part of the structure you would like to replace.
This library is a subset of the medchem library mentioned next.

The larger medchem growing set contains 120k fragments derived from common drug motifs, building blocks and structures observed in PDBs.

• Download the medchem set here.

The sp³ carbon library contains fragments that bear an sp3-hybridized, α carbon atom. This library can be used to grow from heavy atoms such as nitrogen, oxygen, and sulphur to create results that are accessible through nucleophile substitution (e.g. alkylation).

• Download the sp³ set here.

This set features computationally validated hinge binders derived from bioactive molecules. Based on the previously reported Hinge Binder Collection, a FastGrow library was created to support kinase-focused drug discovery projects.
Read more about the hinge binder set following this link.

• Download the hinge binder set here.

This set contains dipeptides made from proteinogenic amino acids connected through amide groups and their bioisosteres. With approximately 661,000 entries, it is ideal for efficient structure-based peptide design within a binding pocket, allowing potential candidates to be identified from a vast array of combinations.

This set represents the N-terminus configuration: after the growing vector, it features the equivalent of the amide nitrogen and concludes with an N-terminal group.

• Download the N-terminus dipeptide set here.

You can read more about the dipeptide set following this link.

This is the C-terminal equivalent of the dipeptide library. After the growing vector, it features the equivalent of the amide carbonyl and concludes with a C-terminal group.

• Download the C-terminus dipeptide set here.

You can read more about the dipeptide set following this link.

The energy minimization of structures and complexes serves as a basis to improve and refine starting points for virtual screenings, dockings, and SAR analyses. Here, an X-ray structure, a model, or a predicted ligand complex is used to calculate a conformer with a lower energy level in a local minimum compared to the initial structure. The YASARA module is an optional extension to SeeSAR.

The feature spotlight on structure optimization can be found following this link.

Find out more about energy minimization in YASARA here (PDF).

Chemical Space Docking™ is a novel structure-based virtual screening method that screens ultra-vast Chemical Spaces for the most promising drug candidates.
Users can conveniently start their C-S-D workflow in SeeSAR's visual interface. HPSee then takes over the calculations and the preparation of the results.
The initial anchoring of synthons can be enhanced by using co-crystallized ligands or predicted binding poses from docking studies as templates for pose generation. This approach accelerates calculations while producing poses that are structurally aligned with the template molecule.

Learn more about C-S-D.
Read more about C-S-D here (PDF).
Learn more about C-S-D in SeeSAR 14 Atlas.

Covalent docking has reached new heights of sophistication within SeeSAR. The augmented version automatically detects covalent warheads in provided ligands and transforms them into their target-bound forms, recognizing the 36 most commonly used warheads.
SeeSAR takes the flexibility of the formed covalent bond into account during the pose generation or can be kept rigid if the exact exit vector is known.
Furthermore, you can conveniently select and sample any residue side chain valid for covalent targeting in the user interface.

With MedChemesis (a word play on "Medicinal Chemistry" and "Mutagenesis") it is possible to mutate your ligand based on conventional transformations used in medicinal chemistry. Starting from a ligand-protein complex, different transformations of the ligand are sampled (e.g., introduction of a methyl group, replacement of a carboxylic acid for a tetrazole, substitution of a carbon atom for a nitrogen atom in an aromatic ring system) and promising suggestions are presented to you.
The perk of this method is that only interesting modifications are generated without the need of enumeration of all possible modifications in every position. As example: Introduction of groups that would lead to molecular clashes with the surface are not sampled. Generation of 50 suggestions can be done within few seconds on standard hardware. MedChemesis can therefore be used to create small sets of compounds to generate ideas for subsequent modifications and to form hypotheses for follow up synthesis.
In its first iteration MedChemesis features 290 commonly-used chemical reactions.

Find out more about MedChemesis here (PDF).

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).

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).

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.

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.

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 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 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).

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).