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Connected Chemistry: Choosing the most promising drug candidates from your hit list

When designing new molecules, research scientists have access to a wealth of information which paves the way for more innovative ideas than ever before. Yet, they also face time constraints to deliver the best possible molecule in the fastest time. Recently, artificial intelligence and machine learning (AI/ML) methods have gained popularity amongst pharmaceutical and biotech organizations, offering a way to expedite the drug discovery process by delivering a large number of possible candidates for any given therapeutic area. However, to capitalize on the opportunity that these offer, medicinal chemistry insight is still needed to triage the hit list of molecules from AI/ML outputs. Scientists need to be able to make informed decisions around which molecules are most likely to bring commercial success, and therefore which hits to progress through the Design-Make-Test-Analyze (DMTA) cycle.

This includes exploring the IP position to ensure that time is not wasted on unpatentable molecules, as well as gauging synthetic feasibility and cost. Chemists need to bring together all the collected data into one place to work most efficiently, however the current diversity in business models and communication channels means that information is spread more widely than ever. Whilst outsourcing to consultants and CROs for both design and synthesis can be a cost-effective way to streamline the discovery process, it often leads to yet more emails, slide presentations and spreadsheets, which have not been developed with chemistry in mind, and are cumbersome to search through to find key information quickly.

Figure 1. By centralizing all key information into a visual, chemistry-aware platform, Torx enables chemists to easily capture and share key insights with internal and CRO teams.

Figure 1. By centralizing all key information into a visual, chemistry-aware platform, Torx enables chemists to easily capture and share key insights with internal and CRO teams.

Torx® addresses these challenges by connecting people with each other, and with information and data across the DMTA cycle, in a visual, interactive cloud-based platform. As a result, chemists are able to track molecules in real time from idea to registration, and use this information to make better, faster decisions to speed up the drug discovery process. Chemists can expand on the intrinsic capabilities within Torx with seamless integration to and from third-party applications, whilst ensuring that all data is centralized in single, accessible location.

Access key insights with CAS SciFindern and Torx Design-Analyze

Focusing on the Design aspect of DMTA, the dedicated CAS SciFindern plugin within Torx Design-Analyze makes it easy for you and your team to determine the right direction for new design ideas. In our webinar ‘Connected Chemistry: Choosing the most promising drug candidates from your hit list’, learn how the plugin enables you to access key insights quickly around IP position and synthetic feasibility, and present these findings alongside other key information such as physico-chemical properties and 3D pose in your Torx design library, to support informed decision making around molecule prioritization. Finally, we show how this information can be saved back centrally into Torx, so that it can be accessed easily by the rest of the team.

Figure 2. Chemists gain quick insights on synthetic feasibility and available IP position from CAS SciFindern through its dedicated plugin in Torx Design.

Figure 2. Chemists gain quick insights on synthetic feasibility and available IP position from CAS SciFindern through its dedicated plugin in Torx Design. By adding the output from CAS SciFindern searches back into Torx, it can be shared easily with select team members and CROs in order to inform compound synthesis decision making.

Please complete the web form to access the full webinar recording or contact us if you’d like to try Torx on your in-house projects.

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