AI-Driven Fragment-Based Drug Discovery: Enhancing Molecular Encoding

By Dr Silpaja Chandrasekar, PhDReviewed by Susha Cheriyedath, M.Sc.Feb 8 2024

In a paper published in the journal Communications Chemistry, researchers delved into the the crossroads of computer science and life sciences: artificial intelligence (AI)-driven small molecule drug discovery. Among the innovative methodologies gaining traction, fragment-based drug discovery (FBDD) emerged as a promising approach.

Leveraging the formidable capabilities of generative pre-trained transformers (GPT) models, which excelled in various domains due to their adeptness in pre-training and learning linguistic fundamentals, researchers explored their application in molecular encoding. Like a chemical language, molecular encoding demanded fragmentation aligned with specific chemical principles to ensure accurate representation.

The review offered a comprehensive insight into the then-current landscape of molecular fragmentation techniques. Systematically summarizing the approaches and applications of various methods, the evaluation highlighted their unique characteristics and applicability while discussing real-world implementations. Additionally, the review presented an outlook on the evolving trends in molecular fragmentation techniques, outlining potential research avenues and addressing associated challenges.

AI in Drug Discovery

In drug discovery, the integration of AI has witnessed significant progress, notably with the breakthrough unveiling of AlphaFold21 by Deepmind, which accurately predicted millions of protein structures. This advancement has catalyzed AI-driven methodologies, particularly in small-molecule drug design. However, the near-term efficacy of such technologies depends on understanding and representing chemical space computationally.  A pivotal aspect of this comprehension involves the systematic fragmentation of compounds to identify key correlations between substructures, laying a robust foundation for subsequent analysis.

FBDD has emerged as a prominent strategy in pursuing novel pharmaceutical compounds, offering a viable alternative to traditional high-throughput screening (HTS) methods. FBDD entails the systematic dissection of complex molecules into smaller fragments, enabling a deeper understanding of structural features crucial for molecular recognition and binding to biological targets. Unlike HTS, FBDD screens fewer compounds but explores a broader chemical space, facilitating the optimization of low molecular weight ligands into potent drug candidates with desirable properties. Moreover, FBDD enhances the likelihood of achieving targeted interactions within binding sites, thus bolstering the efficiency of lead compound identification and optimization processes.

The advent of GPT models has further revolutionized AI-driven drug discovery by offering robust capabilities across various domains. Leveraging the principles of linguistic unit segmentation, GPT models can be applied to molecular fragmentation, thereby enhancing compound representation and facilitating the extraction of semantic relationships between substructures. However, challenges persist in deriving fragment divisions that balance chemical activity representation integrity with manageable lexicon size. Recent research has focused on developing large-scale, non-expertise-dependent fragmentation methods to address these challenges and enhance scalability and applicability in universal drug discovery scenarios.

Molecular Fragmentation Overview

Molecular fragmentation involves dividing large molecular compounds into smaller fragments, which finds widespread applications in computational chemistry, drug design, and chemical informatics. This work focuses on systematically categorizing and organizing methods related to molecular fragmentation, considering various aspects such as the mode of fragmentation, retention of fragmentation information, and utilization of predefined fragment libraries.

Researchers highlighted FBDD as a pivotal strategy in drug design. Low molecular weight polar fragments or compounds are screened against specific targets using biophysical methods like X-ray crystallography and nuclear magnetic resonance. Despite the effectiveness of utilizing existing fragment libraries for molecular fragmentation, challenges persist in ensuring comprehensive coverage of potential molecular fragments.

Furthermore, advancements in computational tools have facilitated the identification of promising fragment hits, often involving the preparation, docking, and hit confirmation of virtual fragment libraries through molecular docking and dynamics simulations. Additionally, sequence-based fragmentation methods like character slicing (CS) and byte-pair encoding (BPE) offer innovative approaches to molecular segmentation, enhancing the extraction of features from molecular tokens.

On the other hand, structure-based fragmentation focuses on breaking down compounds into molecular fragments to identify and optimize critical features of drug molecules. Methods like matched molecular pairs (MMPs) and retrosynthetic combinatorial analysis procedure (RECAP) demonstrate approaches to simulate fragment-linking scenarios and acquire active building blocks for drug design. These diverse fragmentation methodologies provide valuable insights into the molecular composition of compounds, aiding in developing novel therapeutic agents.

Innovative Compound Fragmentation

Recent advancements in compound fragmentation methods have introduced innovative approaches to dissecting molecules into smaller fragments. For example, extended molecular fragmentation (eMolFrag) divides molecules into bricks and linkers, leveraging the breaking retrosynthetically interesting chemical substructures (BRICS) algorithm. In contrast, molecular fragmentation (MacFrag) extends this method with systematic ring-breaking rules.

Realistic ligand molecule (ReLMole) automatically extracts functional groups from atom-level graphs, providing explicit partitioning of compound fragments. At the same time, tree decomposition identifies simple cycles to construct cluster graphs for fragment extraction. Additionally, fragmentation and substructure mining in fragmented relational abstraction (FASMIFRA) identifies cleavable bonds between heavy atoms. These methods and potential approaches, like byte-pair encoding neural language model (BPE_NLM) and vocabulary learning via optimal transport (VOLT), offer diverse strategies for molecular fragmentation, promising advancements in drug design, and chemical informatics.

FBDD Advantages and Considerations

The FBDD method offers several advantages, including higher sensitivity, smaller compound libraries, and high-quality starting points for drug development. FBDD enhances drug efficiency, aids in understanding binding sites, reduces costs, and increases the diversity of drug compounds. However, selecting appropriate molecular fragments involves fragment library size, chemical space sampling, fragment complexity, and diversity. Despite low affinity and specificity challenges in minor fragment hits, they remain valuable for probing protein interactions and drug discovery efforts.

Conclusion

To sum up, FBDD offers numerous advantages in drug development, including enhanced sensitivity, reduced library sizes, and the utilization of high-quality starting points. However, selecting appropriate molecular fragments involves library size, chemical space sampling, and fragment complexity. Despite challenges such as low affinity and specificity in minor fragment hits, they remain valuable for probing protein interactions and advancing drug discovery efforts.