In the critical domain of drug discovery, the capability to swiftly generate, assess, and refine innovative, synthetically viable compounds is revolutionary. ChemPass SynSpace leads this revolution by providing a distinctive combination of rule-based artificial intelligence, professional synthetic chemistry, and powerful cheminformatics to expedite the transition from concept to preclinical candidates. This platform transcends conventional molecular design tools. It is an extensive ecosystem that enables medicinal and computational chemists to explore chemical space with unparalleled speed, assurance, and efficacy.
The Core Technology: AI Meets Synthetic Chemistry
The foundation of SynSpace’s technological stack is rule-based AI, which makes use of an extensive repository of knowledge about synthetic reactions and sophisticated medicinal chemistry concepts. The platform’s algorithms prioritize cost-effectiveness, reagent availability, and synthesis feasibility at every stage, simulating the decision-making process of an experienced medicinal chemist. This method reduces the possibility of creating chemicals that are hard or impossible to synthesize while also speeding up the design process.
SynSpace integrates thorough assessments of functional group tolerance, steric variables, and electronic effects that affect reactivity and selectivity with reaction rules that are carefully designed for every transformation. A semi-automated, literature-driven knowledge analysis method is used in the software training process to make sure that all accessible data is thoroughly examined before transformations are added to the platform. Because of this, SynSpace presently has a catalog of about 300 reaction transformations, each of which is categorized and indexed by distinct reaction numbers for accurate reference.
To guarantee thorough and current reagent coverage, the commercial reagent set in SynSpace is compiled from reagents that are accessible through the two main aggregators, eMolecules and Chemspace. Updates have been included from both sources. This improves the usefulness and translatability of SynSpace’s in silico workflows by enabling users to consistently develop syntheses using commercially available building blocks.
Key Design Modules:
SynSpace offers a comprehensive suite of design modules that cater to every stage of the drug discovery pipeline via different techniques and solutions:
Derivatization Design: A de novo generative design module that allows users to explore novel chemical space by generating patentable molecules based on an input ligand structure. The platform can be configured for broad exploration or focused optimization, making it suitable for both early-stage lead generation and late-stage lead optimization.
Reaction-Based Design Module: Enables users to design molecules by applying known chemical reactions sequentially, ensuring each step is synthetically feasible. It allows exploration of chemical space through realistic reaction pathways, facilitating design of compounds with practical synthetic routes.
Reagent-Based Generative Design Module: A de novo generative design module that leverages automated multi-step reaction sequences for generating novel analogs for input ligand structures, enabling users to build focused compound series around the input structure based on commercially available reagents. It integrates cost, availability, and compatibility filters to ensure synthetic accessibility and practical synthesis scalability.
Library Enumeration Module: The most versatile enumerations tool on the market that supports multi-step synthetic sequences of up to nine steps with multiple reactions in each step, with built-in filters for physicochemical properties, cost, and availability. This module is ideal for generating focused libraries for high-throughput screening and multiparameter optimization in which the synthetic sequence to all compounds are validated by the built-in chemistry rules.
1-Click Scaffold Design Module: Facilitates rapid scaffold replacement, allowing users to explore novel ring systems that match the 3D shape and key pharmacophoric features of the input scaffold. The platform classifies synthetic feasibility into easy and hard subsets, providing users with actionable insights for scaffold hopping.
MacroGrow Module: Enables users to rapidly design small molecule synthetically feasible macrocyclic compounds within protein pockets with diverse linker design starting from fragments or lead-like molecules. Macrocycles are only generated in the pocket areas where there is sufficient special room and orientation to accommodate macrocyclic molecules that preserve the input small molecule binding orientation. This module is particularly useful for hit-to-lead and lead optimization with or without taking advantage of the 3D shape of the protein’s pocket.
Retrosynthesis Module: Provides synthetic route analysis and ranks synthetic solutions based on reagent cost and availability. This module is invaluable for guiding practical synthesis decisions and optimizing synthetic routes for small lead-like molecules.
AutoGrow Module: Enables users to fill protein pockets with synthetically feasible growing of fragments or lead-like molecules, improving binding interactions and optimizing multiple parameters simultaneously. This module is particularly useful for fragment-based lead discovery and hit-to-lead and lead optimization with or without taking advantage of the 3D shape of the protein’s pocket.
Optimal Workflow for Designing and Prediction Using ChemPass SynSpace
1. Learn and master the software use via the available case studies and case study videos provided by ChemPass.
2.Project Setup and Structure Preparation:
- Begin with a high-quality crystal or docking structure of your target protein with an existing ligand (fragment, hit, or lead).
- Upload the prepared PDB file and ligand to SynSpace. The platform maps potential growth vectors or scaffold modification sites based on the ligand binding pose.
3.Initial Exploration with AutoGrow or Derivatization Design:
- Use the AutoGrow module for fragment or lead growing at identified ligand exit vectors within the binding pocket.
- Alternatively, launch the Derivatization Design module for automated de novo generative design focusing on portions of the molecule to be optimized.
- Leave default filters initially; generate a broad set of synthetically feasible compounds.
4.Filtering and Prioritization:
- Apply property filters tuned for drug-like hit characteristics (e.g., MW, logP, H-bond donors/acceptors, TPSA) to narrow down the compound list.
- Use SynSpace’s integration with docking tools or the ChemPass AID platform to rank compounds by docking scores or predicted biological activity.
- Conduct substructure searches to identify promising chemotypes or desired concepts among generated compounds.
5.Scaffold Replacement and Library Enumeration:
- Apply 1-Click Scaffold Design to explore scaffold hopping options that optimize chemical novelty, binding, and synthetic accessibility.
- Apply Derivatization Design to explore scaffold analog options by generative scaffold analoging.
- Generate refined libraries with focused Library Enumeration, leveraging retrosynthetic insights to construct multi-step synthetic sequences tailored to reagent availability and cost.
- Use reagent substructure or similarity searches to populate enumeration with practical building blocks.
6.Retrosynthetic Route Analysis:
- For prioritized molecules, run the Retrosynthesis Module to obtain detailed synthetic routes.
- Evaluate and rank synthesis paths by reagent cost, availability, and step count to select the most efficient synthetic strategies.
7.Iterative Optimization Cycles:
- Repeat design cycles targeting different molecular regions sequentially or simultaneously by adjusting modification flags and options in the design setup.
- At each cycle, utilize SynSpace to generate, filter, and rank new analogs rapidly typically within minutes to a few hours.
- Incorporate external experimental data or docking feedback to refine filter thresholds and design parameters.
8.Macrocyclic and Advanced Design (If Applicable):
- For macrocyclic drug projects, use the MacroGrow module in combination with Derivatization Design to rapidly navigate complex macrocycle synthesis and SAR towards candidate molecules. Both modules handle and process macrocyclic compounds effectively.
- Manually introduce macrocyclization steps into the synthetic sequences in Library Enumeration and apply reagent filters to focus macrocycle libraries.
9.Final Candidate Selection and Export:
- Select top candidates balancing drug-like properties, and synthetic feasibility.
- Advanced CADD, ML and deep learning ranking and selection tools are available in ChemPass’ AID platform for rapid hit to candidate optimization using SynSpace designed molecule sets.
- Export molecules along with synthetic schemes for synthesis planning and experimental validation.
Speed and Efficiency:
SynSpace’s unmatched speed and efficiency is one of its most attractive characteristics. The platform eliminates the need for advanced cheminformatics or synthetic skills because of its user-friendly interface and comprehensive API, which make it accessible to both novice and experienced users. Project managers, medicinal chemists, and computational chemists may work together easily thanks to the democratization of drug discovery technologies, which speeds up the entire drug discovery process. The design jobs and results can easily be shared among members of the project team.
SynSpace’s speed is not just a matter of computational power, it is a result of its intelligent design and optimization algorithms. The platform’s ability to generate and filter thousands even millions of molecules relevant for project progression in a matter of hours, coupled with its integration of synthetic feasibility and reagent data, allows users to focus on the most promising candidates for synthesis. This targeted approach significantly reduces the number of compounds that need to be synthesized and tested, saving time, cost and resources.
The software can be installed in the cloud or in local Linux instances.
Integration with ChemAxon:
By integrating with ChemAxon components, SynSpace expands its capabilities and gives users access to industry-leading standard cheminformatics tools. ChemAxon powers features like chemical sketching, structural checking and standardization, searching, and enumeration, guaranteeing that users may operate with dependable and familiar techniques. This collaboration has drastically accelerated SynSpace’s growth, enabling cloud access and bringing cheminformatics solutions closer to synthetic chemists.
Conclusion:
ChemPass SynSpace is a revolution in drug discovery by fusing artificial intelligence (AI) with the empirical knowledge of synthetic chemistry to provide a dependable and quick platform. It has the potential to completely transform the drug discovery process and lower the time and expense of bringing new medications to market because of its capacity to create, assess, and optimize novel, synthetically feasible compounds in a matter of hours. SynSpace is a shining example of innovation in the realm of drug development, enabling researchers to push the limits of what is feasible in the pursuit of novel and improved medicine.