Query: What do you think of my summary and outlook section?

Summary and Outlook
Fragments offer robust templates for efficient chemical space exploration, formulaic DMT workflows, and (Q)SAR acquisition, helping in the efficient development of potent follow-up compounds with lead-like characteristics. FBDD however has limitations. The initial fragments selection, crucial for shaping the hit-to-lead strategy, typically lacks a clear and objective rationale, signalling an area for future development. It is our view that progresses in fragment selection and systematic screening/ testing data utilisation could help streamline future early-stage drug discovery. The progression of fragments into lead-like follow-up compounds is intrinsically case-specific and multi-objective hence requiring holistic approaches and models accounting for physicochemical properties, binding affinities, synthetic feasibility, and biological activity profiles necessary to optimise drug-like characteristics effectively. 
Defining and refining objectives across different DMT iterations is similarly a case-specific and often subjective process. End-to-end workflows and automated laboratories powered by artificial intelligence for decision-making are expected to help overcome this bottleneck. Fragments, with their modularity, serve as ideal scaffolds for multi-task optimization across these workflows.
In the design phase, fragment data can empower computational methods, particularly those aimed at integrating activity (affinity and ADMET) predictions with chemical accessibility. Evolving fields like machine learning and free energy calculations, where fragment data can be used to guide models and simulations, are also showing promise to better prioritise follow-up compound in-silico for synthesis and testing61,84. Generative AI is particularly powerful for sampling ultra-large chemical spaces and addressing multi-parameter optimization challenges. However, there are still significant pitfalls, including chemical stability considerations112. Newly established active learning protocols for transforming costly physics-based scoring into more efficient ligand-based approaches are showing promise, with fragment data potentially enhancing predictions by providing high-quality reference poses and scaffolds.
The “Make” phase in FBDD is also evolving. Novel synthetic methodologies, in particular C-H activation, provide access to previously unexplored chemical spaces95. Advances in high-throughput instrumentation and automation, facilitate the synthesis of larger follow-up arrays, thereby allowing for more extensive explorations91. These large-scaled and intensive explorations can be harnessed by computational methods for QSAR model building or the machine-learning-based optimisation of synthetic tasks, therefore better guiding subsequent DMT steps and improving compound design. Fragments serve as high-quality starting points for synthetic elaboration, but the large-scale production of compound arrays can lead to bottlenecks, particularly during purification4,92,93. Retrosynthetic algorithms are increasingly valuable for compound scoring for accessibility, offering pathways to identify compounds derived from fragments or analogues, while constraining routes to achieve more feasible and efficient synthesis plans.
Testing methodologies, such as crystallography, are adapting, especially with the introduction of testing crude reaction mixtures from fragment elaboration and other purification-agnostic methods, like DELs93,108,110. It is our view that these advances bring about the challenge of deconvoluting noisy experimental data to build reliable (Q)SAR models, highlighting the need for sensitive testing methods, usage of complementary modelling tools, and interpretable data analysis techniques.
Overall, the usage of fragments in the hit-to-lead process is brimming with opportunities at each stage of DMT cycles, fostering an integrative approach to identify potent lead-like follow-up compounds. Significant advances in design, synthetic and testing methods have made possible to more efficiently explore high-quality chemical spaces, using fragments as high-quality seeds leading to the acquisition of vast SAR data that can be employed to re-enforce the productivity of subsequent DMT cycles. However numerous bottlenecks remain to be addressed especially in fragment selection and appropriate use of fragment data to guide physics-based simulation and machine learning approaches. There are also bottlenecks in how to efficiently pair high-throughput synthetic approaches with testing methodologies to rapidly extract reliable SAR models from which lead compounds can be designed.

Subject: chemistry
Intent: Paper Understanding