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Virtual screening (VS) on small molecules aims to identify promising drug candidates against protein targets from expansive chemical libraries by balancing the core requirements of accurate scoring and efficient search against the inherent trade‐off between accuracy and speed. This survey provides a comprehensive review of how Artificial Intelligence and Machine Learning (AI/ML) are redefining this landscape across three critical dimensions. First, we examine the evolution of AI‐driven scoring functions, which utilize AI/ML models to capture complex structure–activity relationships from massive biochemical datasets, significantly enhancing structure‐ and ligand‐based evaluations beyond traditional heuristics. Second, we summarize the emergence of efficient search algorithms that iteratively prioritize informative compounds to reduce search efforts by orders of magnitude. Third, we review the paradigm shift toward generative molecular design, making VS transition from screening fixed libraries to the de novo generation of molecules optimized for specific structural contexts and multi‐objective properties. This review outlines the transition toward end‐to‐end, adaptive discovery systems that ensure computational hits are biologically potent, structurally optimized, and synthetically accessible.

Boltz-2 has enabled accurate binding affinity prediction by leveraging co-folded protein–ligand structures, but the absence of a public training recipe has limited its use in active drug discovery projects, where new experimental measurements and congeneric ligand series continually arrive during lead optimization. We present an open framework for affinity fine-tuning of Boltz-2, showing that adapting only the affinity prediction components with project-specific experimental data can make the model substantially more useful for lead optimization. We evaluate the approach in two internal studies: a multi-target retrospective benchmark against physics-based and machine learning baselines, and a large single-target study with up to 1,700 ligands. In both settings, fine-tuned Boltz-2 improves correlation over the off-the-shelf model, and in some cases reaches performance competitive with free energy perturbation (FEP) methods. By releasing this framework, we aim to enable the community to adapt Boltz-2 to the specific targets and data of their own drug discovery campaigns. The code is available at: https://github.com/molecularinformatics/Boltz2_affinity

Bruton's tyrosine kinase (BTK) is a nonreceptor tyrosine kinase clinically validated to impact B-cell development. Molecules designed to target BTK, through either covalent or reversible inhibition, have transformed the treatment of hematopoietic malignancies. Wen, T.; Wang, J.; Shi, Y.; Qian, H.; Liu, P. Inhibitors targeting Bruton's tyrosine kinase in cancers: drug development advances. Leukemia 2021, 35(2), 312-332.10.1038/s41375-020-01072-6. These advancements are paving the way for new therapeutics to treat nononcology indications, De Bondt, M.; Renders, J.; Struyf, S.; Hellings, N. Inhibitors of Bruton's tyrosine kinase as emerging therapeutic strategy in autoimmune diseases. Autoimmun. Rev. 2024, 23(5), 103532.10.1016/j.autrev.2024.103532 such as multiple sclerosis (MS), and provide benefits to patients with progressive disease. In this context, we describe the discovery of a highly selective, CNS-penetrant, reversible BTK inhibitor designed to sequester Tyr-551, the critical phosphorylation site, into an inactive conformation, thereby blocking B-cell receptor (BCR) signaling. While this class of molecules demonstrated excellent safety when administered at doses that fully inhibited B-cell activity in the periphery, increasing exposure to achieve similar efficacy in the CNS led to adverse findings. This raises the question of whether it was a molecule-specific off-target toxicity or a consequence of blocking BTK function in microglia.

1. Monoclonal antibodies (mAbs) have emerged as one of the most successful classes of biotherapeutics, with over 200 FDA-approved antibodies currently in clinical use.1,2 Their high specificity and...

Molecular alignment and 3D similarity are crucial tasks in computational drug discovery, enabling applications such as virtual screening and pharmacophore modeling. ROSHAMBO, an open-source package for optimizing molecular alignment using Gaussian volume overlaps, demonstrated near-state-of-the-art performance and accuracy across multiple target classes. However, its computational efficiency has been a limiting factor in the virtual screening of ultralarge chemical libraries. To address this limitation, we introduce ROSHMABO2, an optimized version that achieves a greater than 200-fold improvement in performance over the original ROSHAMBO implementation through algorithmic innovations, GPU acceleration, and optimized memory handling. This performance establishes ROSHMABO2 as an ideal tool for high-throughput applications, such as virtual screening and chemical library design, enabling efficient exploration of large chemical spaces. In addition to its computational enhancements, the new version retains its modularity, accessibility, and compatibility with diverse workflows. These improvements position ROSHAMBO2 as a transformative tool for modern cheminformatics, addressing the growing demands for scalable molecular modeling. ROSHAMBO2 is accessible at https://github.com/molecularinformatics/roshambo2 and is available for use under the MIT license.

Covalent BTK-inhibitor drugs often contain reactive acrylamide warheads designed to irreversibly bind to their protein targets at free thiol cysteines in the kinase active site. This reactivity also makes covalent inhibitors susceptible to conjugation to endogenous tripeptide glutathione (GSH), leading to clearance. During lead optimization efforts for the drug discovery of covalent BTK inhibitor BIIB129, some expected GSH adducts resulted in an unexpected and highly abundant rearrangement fragment ion in LC-MS/MS. By examining more than 30 inhibitors, the rearrangements were found to be dependent on the presence of a cycloalkane linker that connects the warhead to the kinase hinge binder motif of drug molecules. The proposed mechanism includes the formation of a 16-membered macrocyclic intermediate between the γ-glutamic acid residue (Glu) of GSH and a methyl-cyclobutyl cation, resulting in a rearrangement fragment originating from two distant parts of the adduct molecule separated by the warhead conjugated with the cysteine residue in between. Rich sets of chemical analogues available during the lead optimization enabled confirmation of the macrocyclic rearrangement. Proposed macrocyclic rearrangement was verified using GSH derivatives: N-acetylation of the γ-Glu blocked the rearrangement, and esterification of the γ-Glu side chain resulted in an expected shift in the mass of rearranged fragment ion. Proposed rearranged ion structures were supported by MS³ and MS⁴ fragmentations. Comparisons of the ion fragmentation of GSH conjugates between cis and trans matched pairs suggest a concerted mechanism for the cyclobutane linker and a stepwise mechanism for the methylcyclobutane linker, respectively.

Efficient virtual screening techniques are critical in drug discovery for identifying potential drug candidates. We present an open-source package for molecular alignment and 3D similarity calculations optimized for large-scale virtual screening of small molecules. This work parallels widely used proprietary tools and offers an approach complementary to structure-based virtual screening. Our package employs the PAPER software for optimizing molecular alignments based on Gaussian volume overlaps. GPU acceleration is utilized to significantly reduce computational time and resource requirements. After obtaining the optimal alignments between the target and the query molecules, both shape and color (based on pharmacophore features) scores are computed to assess molecular similarity, with aligned molecules optionally being output in sdf format. The package was benchmarked using the DUDE-Z public data sets. Results demonstrated the package's near-state-of-the-art performance and robustness across multiple target classes, with speed that enables many routine ligand-based drug discovery workflows. As an open-source and freely available resource (github.com/molecularinformatics/roshambo) with both a convenient Python API and command line interface, our package also addresses the need for accessible and efficient virtual screening tools in drug discovery.

Efficient virtual screening techniques are critical in drug discovery for identifying potential drug candidates. We present an open-source package for molecular alignment and 3D similarity calculations optimized for large-scale virtual screening of small molecules. This work parallels widely used proprietary tools and offers an approach complementary to structure-based virtual screening. Our package employs the PAPER software for optimizing molecular alignments based on Gaussian volume overlaps. GPU acceleration is utilized to significantly reduce computational time and resource requirements. After obtaining the optimal alignments between the target and the query molecules, both shape and color (based on pharmacophore features) scores are computed to assess molecular similarity, with aligned molecules optionally being output in sdf format. The package was benchmarked using the DUDE-Z public datasets. Results demonstrated the package's near-state-of-the-art performance and robustness across multiple target classes, with speed that enables many routine ligand-based drug discovery workflows. As an open-source and freely available resource (github.com/molecularinformatics/roshambo) with both a convenient Python API and command line interface, our package also addresses the need for accessible and efficient virtual screening tools in drug discovery.

Multiple sclerosis (MS) is a chronic disease with an underlying pathology characterized by inflammation-driven neuronal loss, axonal injury, and demyelination. Bruton’s tyrosine kinase (BTK), a nonreceptor tyrosine kinase and member of the TEC family of kinases, is involved in the regulation, migration, and functional activation of B cells and myeloid cells in the periphery and the central nervous system (CNS), cell types which are deemed central to the pathology contributing to disease progression in MS patients. Herein, we describe the discovery of BIIB129 (25), a structurally distinct and brain-penetrant targeted covalent inhibitor (TCI) of BTK with an unprecedented binding mode responsible for its high kinome selectivity. BIIB129 (25) demonstrated efficacy in disease-relevant preclinical in vivo models of B cell proliferation in the CNS, exhibits a favorable safety profile suitable for clinical development as an immunomodulating therapy for MS, and has a low projected total human daily dose.