Fix the formatting of the following text. ONLY return the modified text and nothing else.



**Key Points:**

  

* **Reagent Selection Strategy:** The group is discussing a strategy to reduce the number of reagents tested from 150,000 to a diverse subset of 4,000.  This smaller set will be used for an initial run to test the process before scaling up.  The 4000 reagents were selected for maximum diversity in chemical properties.

  

* **Two-Stage Run:** A two-stage approach is proposed: a smaller initial run with 4,000 reagents to validate the process, followed by a larger run with the remaining reagents if the first stage is successful. This minimizes risk and reduces computational cost initially.

  

* **Filtering for Developable Molecules:** The group confirmed that all reagents and potential add-ons are developable, meaning they have desirable drug-like properties.  However, they need to consider whether all possess good "aim scores" and other relevant properties,  which could be used to further filter the list.

  

* **Computational Cost and Parallel Processing:** Running all 150,000 reagents at once would result in a massive 3 billion outputs. The two-stage approach mitigates this. Cluster labels will be generated "on-flight" (during the generation step) to improve efficiency.

  

* **Drug-like Properties and Filtering:** The group decided to use KECO2, HLN, and other relevant properties (such as logS from RDKit) as filters to select for molecules with desirable drug-like properties. KECO2 will be the primary filter due to its computational cost and ability to significantly reduce the number of candidates.

  

* **OKRs for the Quarter:**  The group discussed setting Objectives and Key Results (OKRs) for the quarter, focusing on increasing predictability, optimizing ML strategy, increasing nominations, improving accessibility, strengthening knowledge management, and target deconvolution.  Specific action items within OKRs need to be defined.

  

* **Data Organization and Querying:** The group discussed strategies for organizing the massive dataset output.  Clustering-based partitioning was discussed as a potential approach to improve querying efficiency. A consultation with Rhodian Thomas was suggested to refine this strategy.

  

  

**Next Steps and Action Items:**

  

1. **Initial Run with 4,000 Reagents:** Conduct a test run with the 4,000 diverse reagents.

2. **Analysis and Testing:** After the initial run, analyze the results and test the methodology. Cost is not a major concern at this initial stage.

3. **Filter Selection and Application:** Generate KECO2, HLN, and other relevant properties (including RDKit drug-like properties like logP and rule of five violations) to filter the molecules sequentially.

4. **Post-processing Optimizations:** Generate cluster labels "on-flight" during the generation step to improve efficiency.  Explore the use of KECO2 as a primary filter and the optimal approach for data organization to improve queryability. Consult with Rhodian Thomas for clustering strategies.

5. **Cost Estimate:**  Postpone the detailed cost estimate until after the initial run and filtering, when the final number of molecules is known.  However, any runs costing over $5,000 require approval and notification.

6. **OKR Definition:**  Finalize the OKRs for the quarter, including specific action items related to learnings, chemistry cleanup, automation, compound set analysis, pathway scoring, and lab data automation. This will involve clarifying the "still struggling" point around diversity analysis.

7. **Communication and Collaboration:** Aithen to share conclusions about promiscuous or cytotoxic compounds, if any, with the team.  The team will collaborate to put together a Notion document for the OKRs and iterate before the next ops meeting (Monday at 11 am).

8. **Data Organization:**  The output will be broken down into chunks.  Determine the optimal way to organize the data (folders or other methods) to balance efficiency and maintainability.  The final decision on this will be made by the team after considering the queryability implications.