We demonstrate this capability by using CovSite

The gap

Evidence profile

Stated in the future work and abstract and cells research gap sections of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 4 journals. Those papers have been cited 406 times in total.

Supporting evidence — 8 representative gaps

• AlphaFold3 versus experimental structures: assessment of the accuracy in ligand-bound G protein-coupled receptors (2024) · Acta Pharmacologica Sinica · cited 81× · doi

  Understanding ligand-protein interactions is essential for advancing drug discovery, protein design, and structural biology. Therefore, modeling ligand-protein interactions is a cornerstone of modern drug discovery and molecular biology, enabling the rational design of therapeutics and a deeper under- standing of biological processes [59]. Current commercial tools, such as AlphaFold3, have established a high standard for predicting the 3D structures of biomolecular complexes [6, 60]. However, their ac- cessibility and high costs limit broader adoption. The recently introduced fully open-source model, such as Boltz-1, addresses this issue and competes with these state-of-the-art tools in both accuracy and usability. We found that Boltz-1 demonstrated strong performance in reproducing protein folding, deriving its capabilities from the parent source code of AlphaFold 3. Boltz-1 effectively reproduces the 3D structures of biomolecular complexes, demonstrating excellent performance, particularly in docking ligands with flexible macrocycles. It successfully re-docked a diverse set of ligands with varying com- plexities, achieving binding scores comparable to commercial tools such as Glide by Schrodinger. In terms of RMSD ligand-binding ranking, Botlz-1 outperformed the popular docking tool AutoDock Vina for all ligands studied. Finally, while capturing the binding modes of low-molecular-weight or - ganics, predicting complex peptidomimetics remains beyond the current capabilities of Boltz-1. To summarize, our benchmarking indicates that Botlz-1 presents opportunities to improve computational 66 M. V. Prud, A. Kyrychenko screening of small molecular libraries and may play a significant role in the future of AI-driven pre- dicting of ligand-protein interactions. Finally, when our manuscript was ready for submission, Boltz’s developing team announced the revolutionizing update for Boltz-2 (https://github.com/jwohlwend/boltz), introducing controllability features including experimental method conditioning, distance constraints, and multi-chain template integration for structure prediction, and the AI model to approach the performance of free-energy per- turbation (FEP) methods in estimating small molecule–protein binding affinity. A.V.K. acknowledges the grant № 87/0062 (2021.01/0062) “Molecular design, synthesis and screening of new potential antiviral pharmaceutical ingredients for the treatment of infec- tious diseases COVID-19” from the National Research Foundation of Ukraine.

  generalstated in future workevidence 5/5
  Keywords: boltz protein ligand molecular binding interactions design tools performance ligands drug discovery biology current commercial
• Structure and dynamics in drug discovery (2024) · npj Drug Discovery. · cited 65× · doi

  In the last decades, with all the successes and challenges, the transformation from computer-aided drug discovery to computer-driven drug discovery is emerging. The rapid advancements in structural biology along with the bloom of computational protein-structure prediction, are allowing for access to many more high-resolution 3D structures of novel drug-receptor complexes. Dynamic-based drug discovery strategies, coupled with advanced sampling methods, are offering new possibilities for drug design. During the early hit identification stage, the ultra-scale virtual screening approaches, both structure-based and AI-based, are becoming ready in providing fast and cost-effective start points into drug discovery campaigns. The rapid expansion of accessible chemical space, together with the dra- matic increases in computational power such as GPUs and cloud com- puting, are resulting in the ability to virtually screen multi-billions of drug- like chemical space. At the hit-to-lead stage, the more elaborate potency prediction tools such as free energy perturbation and AI-based QSAR often guide rational optimization of ligand potency. In the end, data-driven computational tools are used for multi-parameter optimization, including solubility, permeability, and pharmacokinetic properties, to identify the final drug candidate. It is evident that these breakthroughs are converging towards a new era of computer-driven drug discovery (Fig. 4).

  generalstated in future workevidence 5/5
  Keywords: drug discovery based computer driven computational rapid structure prediction stage chemical space multi potency tools
• Structure and dynamics in drug discovery (2024) · npj Drug Discovery. · cited 65× · doi

  Fig. 1 | Schematic of structure-based drug discovery. The computational methods are usually employed in two stages of SBDD. The virtual screening is widely adapted in the initial hit molecule searching, and many predictive models are used in the design-synthesis-test cycle. Fig. 2 | The increasing of available structures. The rapid expansion of drug target structures in both the Protein Data Bank and AlphaFold database, which has significantly increased opportunities for dis- covering new drugs. This field has grown rapidly in the ensuing years, due in part to the elucidation of the structures of thousands of proteins, nucleic acids, and other potential drug targets27. In recent years, due to leaps in structural biology, including automation in crystallography15, microcrystallography14, and cryo-electron microscopy technology16,17,28, the 3D structures for many clinically important targets have been revealed, often in a state relevant to its biological function (Fig. 2). Especially impressive has been the recent structural revolution for G protein-coupled receptors (GPCRs)29, ion channels30,31 and other membrane proteins that mediate the action of more than half of drugs32, providing excellent targets for ligand screening and lead optimization. The number of target structures has increased significantly with the arrival of machine learning tools such as AlphaFold, which reliably predict the atomic structure of proteins for which experimental structures may not be available13. Since its launch in 2021, the AlphaFold Protein Structure Database has had released over 214 million unique protein structures33, compared to around 200,000 PDB structures corresponding to approxi- mately 60,000 unique protein sequences (Fig. 2). This new data set almost covers the complete UniProt database. Additionally, AlphaFold models can cover the entire length of protein sequences compared to the fragmented, often short coverage of PDB entries. Clearly, researchers and pharmaceu- tical companies can try structure-based drug discovery techniques using these models, presenting unprecedented opportunities for targets without a prior experimental structure.

  generalstated in future workevidence 5/5
  Keywords: structures protein structure drug alphafold targets models database proteins based discovery screening available target signi
• Structure and dynamics in drug discovery (2024) · npj Drug Discovery. · cited 65× · doi

  enzymes, using structure-based virtual screening or molecular docking, with the test molecules chosen from libraries of compounds or analogs of known binders34. The quality of binding of each test molecule was typically deter- mined using model potential energy functions chosen to balance speed and accuracy34. Docking molecules of a virtual drug-like compound library into a target receptor structure and predicting its binding score is a major step in a structure-based drug discovery campaign, which plays a key role in any successful application7,29,35. The predicted candidate ligand sets, produced by such virtual screening, usually show useful hit rates, about 10%-40% in experimental testing36. Some novel hits may also exhibit noteworthy potencies, in the 0.1–10-μM range, for different types of targets36. Special attention has been devoted to ligand scoring functions, which are supposed to reliably select top binders and to rule out false-positive predictions. This is especially important with the growth of library size. For example, a one-in-a-million rate of false positives in a billion-compound library would result in a thousand false hits, which obviously complicates the selection of hit candidates. Another major challenge is the computation cost. With increasing library sizes, the computational time of docking itself is the main bottleneck in virtual screening processes. Nowadays, screenings on ultra-large virtual libraries that include billions of drug-like compounds are feasible, thanks to the recent availability of cloud computing and graphics processing unit (GPU) computing resources36.

  generalstated in future workevidence 5/5
  Keywords: virtual library structure screening docking drug false using based test molecules chosen libraries compounds binders
• Structure and dynamics in drug discovery (2024) · npj Drug Discovery. · cited 65× · doi

  Future directions In the last decades, with all the successes and challenges, the transformation from computer-aided drug discovery to computer-driven drug discovery is emerging. The rapid advancements in structural biology along with the bloom of computational protein-structure prediction, are allowing for access to many more high-resolution 3D structures of novel drug-receptor complexes. Dynamic-based drug discovery strategies, coupled with advanced sampling methods, are offering new possibilities for drug design. During the early hit identification stage, the ultra-scale virtual screening approaches, both structure-based and AI-based, are becoming ready in providing fast and cost-effective start points into drug discovery campaigns. The rapid expansion of accessible chemical space, together with the dra- matic increases in computational power such as GPUs and cloud com- puting, are resulting in the ability to virtually screen multi-billions of drug- like chemical space. At the hit-to-lead stage, the more elaborate potency prediction tools such as free energy perturbation and AI-based QSAR often guide rational optimization of ligand potency. In the end, data-driven computational tools are used for multi-parameter optimization, including solubility, permeability, and pharmacokinetic properties, to identify the final drug candidate. It is evident that these breakthroughs are converging towards a new era of computer-driven drug discovery (Fig. 4).

  generalstated in future workevidence 5/5
  Keywords: drug discovery based computer driven computational rapid structure prediction stage chemical space multi potency tools
• Structure and dynamics in drug discovery (2024) · npj Drug Discovery. · cited 65× · doi

  Alon, A. et al. Structures of the σ2 receptor enable docking for bioactive ligand discovery. Nature 600, 759–764 (2021). 41. Gorgulla, C. et al. An open-source drug discovery platform enables 42. ultra-large virtual screens. Nature 580, 663–668 (2020). Sadybekov, A. A. et al. Synthon-based ligand discovery in virtual libraries of over 11 billion compounds. Nature 601, 452–459 (2022). Tomberg, A. & Boström, J. Can easy chemistry produce complex, diverse, and novel molecules? Drug Discov. Today 25, 2174–2181 (2020). 44. Gorgulla, C. et al. A multi-pronged approach targeting SARS-CoV-2 43. 45. proteins using ultra-large virtual screening. Iscience 24, 102021 (2021). Patel, H. et al. SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules. Sci. data 7, 384 (2020). 65. Miao, Y. et al. Accelerated structure-based design of chemically diverse allosteric modulators of a muscarinic G protein-coupled receptor. Proc. Natl Acad. Sci. 113, E5675–E5684 (2016). Seitz, C. et al. Targeting tuberculosis: Novel scaffolds for inhibiting cytochrome bd oxidase. J. Chem. Inf. Model. (2024). 66. 67. Wong, C. F. & McCammon, J. A. J. Computer simulation and the design of new biological molecules. Isr. J. Chem. 27, 211–215 (1986). 68. Wong, C. F. & McCammon, J. A. Dynamics and design of enzymes 69. and inhibitors. J. Am. Chem. Soc. 108, 3830–3832 (1986). Jorgensen, W. L., Buckner, J. K., Boudon, S. & Tirado‐Rives, J. Efficient computation of absolute free energies of binding by computer simulations. Application to the methane dimer in water. J. Chem. Phys. 89, 3742–3746 (1988). 46. McCammon, J. A., Gelin, B. R. & Karplus, M. Dynamics of folded 70. Gilson, M. K., Given, J. A., Bush, B. L. & McCammon, J. A. The proteins. nature 267, 585–590 (1977). 47. Durrant, J. D. & McCammon, J. A. Molecular dynamics simulations 48. 49. 50. and drug discovery. BMC Biol. 9, 1–9 (2011). Lin, J.-H., Perryman, A. L., Schames, J. R. & McCammon, J. A. Computational drug design accommodating receptor flexibility: the relaxed complex scheme. J. Am. Chem. Soc. 124, 5632–5633 (2002). Amaro, R. E., Baron, R. & McCammon, J. A. An improved relaxed complex scheme for receptor flexibility in computer-aided drug design. J. Comput. -Aided Mol. Des. 22, 693–705 (2008). Lins, R. D. et al. Molecular dynamics studies on the HIV-1 integrase catalytic domain. Biophys. J. 76, 2999–3011 (1999). 51. Goldgur, Y. et al. Three new structures of the core domain of HIV-1 integrase: an active site that binds magnesium. Proc. Natl Acad. Sci. 95, 9150–9154 (1998). 52. Goldgur, Y. et al. Structure of the HIV-1 integrase catalytic domain complexed with an inhibitor: a platform for antiviral drug design. Proc. Natl Acad. Sci. 96, 13040–13043 (1999). Schames, J. R. et al. Discovery of a novel binding trench in HIV integrase. J. Med. Chem. 47, 1879–1881 (2004). 53. 56. 55. 54. Hazuda, D. J. et al.

  generalstated in future workevidence 5/5
  Keywords: mccammon drug design chem discovery receptor nature dynamics integrase virtual complex novel proc natl acad
• CovSite: A High-Throughput Blind Covalent Screening Framework for Reactive Site Detection (2026) · bioRxiv · doi

  We demonstrate this capability by using CovSite as a blind, ligand-specific approach that enables iterative, machine-learning-driven covalent inhibitor generation that is impractical with existing tools, establishing a foundation for computationally guided covalent drug discovery for novel and understudied targets.

  generalstated in abstractevidence 5/5
  Keywords: covalent demonstrate capability using covsite blind ligand specific approach enables iterative machine learning driven inhibitor
• Integrative strategies in herbal product-based Drug discovery: from computational models to molecular insights (2026) · Beni-Suef University Journal of Basic and Applied Sciences · doi

  The conventional drug discovery process is associated with high costs, lengthy development timelines, and high failure rates. - Challenges such as compound isolation, screening, and structural characterization limit the broader applications of natural products. - There is a need for innovative discovery strategies that can address disease complexity at the systems level.

  generalstated in cells research gapevidence 5/5
  Keywords: conventional drug discovery process associated high costs lengthy

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