Harries, L. W. RNA biology provides new therapeutic targets for human disease. Front. Genet. 10, 205 (2019).
Google Scholar
Zhu, Y., Zhu, L., Wang, X. & Jin, H. RNA-based therapeutics: an overview and prospectus. Cell Death Dis. 13, 644 (2022).
Google Scholar
Yu, A.-M., Choi, Y. H. & Tu, M.-J. RNA drugs and RNA targets for small molecules: principles, progress, and challenges. Pharmacol. Rev. 72, 862–898 (2020).
Google Scholar
Lightfoot, H. L. & Smith, G. F. Targeting RNA with small molecules-a safety perspective. Br. J. Pharmacol. 182, 4201–4220 (2023).
Bennett, C. F. Therapeutic antisense oligonucleotides are coming of age. Annu. Rev. Med. 70, 307–321 (2019).
Google Scholar
Luther, D., Lee, Y., Nagaraj, H., Scaletti, F. & Rotello, V. Delivery approaches for crispr/cas9 therapeutics in vivo: advances and challenges. Expert Opin. Drug Deliv. 15, 905–913 (2018).
Google Scholar
Wu, P. Inhibition of RNA-binding proteins with small molecules. Nat. Rev. Chem. 4, 441–458 (2020).
Google Scholar
Childs-Disney, J. L. et al. Targeting RNA structures with small molecules. Nat. Rev. Drug Discov. 21, 736–762 (2022).
Google Scholar
Howe, J. A. et al. Selective small-molecule inhibition of an RNA structural element. Nature 526, 672–677 (2015).
Google Scholar
Dibrov, S. M. et al. Hepatitis C virus translation inhibitors targeting the internal ribosomal entry site: miniperspective. J. Med. Chem. 57, 1694–1707 (2014).
Google Scholar
Palacino, J. et al. Smn2 splice modulators enhance u1–pre-mRNA association and rescue sma mice. Nat. Chem. Biol. 11, 511–517 (2015).
Google Scholar
Ratni, H. et al. Discovery of risdiplam, a selective survival of motor neuron-2 (smn2) gene splicing modifier for the treatment of spinal muscular atrophy (SMA). J Med Chem. 61, 6501–6517 (2018).
Gresh, N. et al. Addressing the issues of non-isotropy and non-additivity in the development of quantum chemistry-grounded polarizable molecular mechanics. in Quantum Modeling of Complex Molecular Systems, 1–49 (Springer, 2015).
Jing, Z. et al. Polarizable force fields for biomolecular simulations: recent advances and applications. Annu. Rev. Biophys. 48, 371–394 (2019).
Google Scholar
Shi, Y., Ren, P., Schnieders, M. & Piquemal, J.-P. Polarizable force fields for biomolecular modeling. Rev. Comput. Chem. 28, 51–86 (2015).
Melcr, J. & Piquemal, J.-P. Accurate biomolecular simulations account for electronic polarization. Front. Mol. Biosci. 6, 143 (2019).
Google Scholar
El Khoury, L. et al. Computationally driven discovery of SARS-CoV-2 M pro inhibitors: from design to experimental validation. Chem. Sci. 13, 3674–3687 (2022).
Google Scholar
Ponder, J. W. et al. Current status of the amoeba polarizable force field. J. Phys. Chem. B 114, 2549–2564 (2010).
Google Scholar
Zhang, C. et al. Amoeba polarizable atomic multipole force field for nucleic acids. J. Chem. Theory Comput. 14, 2084–2108 (2018).
Google Scholar
Gresh, N., Cisneros, G. A., Darden, T. A. & Piquemal, J.-P. Anisotropic, polarizable molecular mechanics studies of inter-and intramolecular interactions and ligand- macromolecule complexes. a bottom-up strategy. J. Chem. Theory Comput. 3, 1960–1986 (2007).
Google Scholar
El Hage, K., Piquemal, J.-P., Hobaika, Z., Maroun, R. G. & Gresh, N. Substituent-modulated affinities of halobenzene derivatives to the HIV-1 integrase recognition site. Analyses of the interaction energies by parallel quantum chemical and polarizable molecular mechanics. J. Phys. Chem. A 118, 9772–9782 (2014).
Google Scholar
Adjoua, O. et al. Tinker-hp: Accelerating molecular dynamics simulations of large complex systems with advanced point dipole polarizable force fields using GPUs and multi-GPU systems. J. Chem. Theory Comput. 17, 2034–2053 (2021).
Google Scholar
Lagardère, L., Aviat, F. & Piquemal, J.-P. Pushing the limits of multiple-time-step strategies for polarizable point dipole molecular dynamics. J. Phys. Chem. Lett. 10, 2593–2599 (2019).
Google Scholar
Jaffrelot-Inizan, T. et al. High-resolution mining of SARS-CoV-2 main protease conformational space: Supercomputer-driven unsupervised adaptive sampling. Chem. Sci. 12, 4889–4907 (2021).
Google Scholar
Célerse, F. et al. An efficient Gaussian-accelerated molecular dynamics (GAMD) multilevel enhanced sampling strategy: application to polarizable force fields simulations of large biological systems. J. Chem. Theory Comput. 18, 968–977 (2022).
Google Scholar
Célerse, F., Lagardère, L., Derat, E. & Piquemal, J.-P. Massively parallel implementation of steered molecular dynamics in tinker-hp: Comparisons of polarizable and non-polarizable simulations of realistic systems. J. Chem. Theory Comput. 15, 3694–3709 (2019).
Google Scholar
Lagardère, L. et al. Lambda-abf: Simplified, portable, accurate, and cost-effective alchemical free-energy computation. J. Chem. Theory Comput. 20, 4481–4498 (2024).
Google Scholar
Blazhynska, M. et al. Water–glycan interactions drive the SARS-CoV-2 spike dynamics: insights into glycan-gate control and camouflage mechanisms. Chem. Sci. 15, 14177–14187 (2024).
Google Scholar
Wang, L. et al. Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J. Am. Chem. Soc. 137, 2695–2703 (2015).
Google Scholar
Zhang, C.-H. et al. Potent noncovalent inhibitors of the main protease of sars-cov-2 from molecular sculpting of the drug perampanel guided by free energy perturbation calculations. ACS Cent. Sci. 7, 467–475 (2021).
Google Scholar
Chodera, J. D. et al. Alchemical free energy methods for drug discovery: progress and challenges. Curr. Opin. Struct. Biol. 21, 150–160 (2011).
Google Scholar
El Hage, K. et al. Targeting the major groove of the palindromic d (ggcgcc) 2 sequence by oligopeptide derivatives of anthraquinone intercalators. J. Chem. Inf. Model. 62, 6649–6666 (2022).
Google Scholar
Gresh, N. et al. Enforcing local DNA kinks by sequence-selective trisintercalating oligopeptides of a tricationic porphyrin: a polarizable molecular dynamics study. ChemPhysChem 25, e202300776 (2024).
Google Scholar
El Hage, K., Mondal, P. & Meuwly, M. Free energy simulations for protein ligand binding and stability. Mol. Sim. 44, 1044–1061 (2018).
Google Scholar
Rasouli, A., Pickard IV, F. C., Sur, S., Grossfield, A. & Işık Bennett, M. Essential considerations for free energy calculations of RNA-small molecule complexes: lessons from the theophylline-binding RNA aptamer. J. Chem. Inf. Model. 65, 223–239 (2025).
Abramyan, A. M. et al. Accurate physics-based prediction of binding affinities of RNA- and DNA-targeting ligands. J. Chem. Inf. Model. 65, 1392–1403 (2025).
Clark, F., Robb, G., Cole, D. J. & Michel, J. Comparison of receptor–ligand restraint schemes for alchemical absolute binding free energy calculations. J. Chem. Theory Comput. 19, 3686–3704 (2023).
Google Scholar
Salari, R., Joseph, T., Lohia, R., Hénin, J. & Brannigan, G. A streamlined, general approach for computing ligand binding free energies and its application to GPCR-bound cholesterol. J. Chem. Theory Comput. 14, 6560–6573 (2018).
Google Scholar
Zhang, H. et al. Accurate estimation of the standard binding free energy of netropsin with DNA. Molecules 23, 228 (2018).
Google Scholar
Gapsys, V. et al. Accurate absolute free energies for ligand–protein binding based on non-equilibrium approaches. Commun. Chem. 4, 61 (2021).
Google Scholar
Dibrov, S. M. et al. Structure of a hepatitis C virus RNA domain in complex with a translation inhibitor reveals a binding mode reminiscent of riboswitches. Proc. Natl Acad. Sci. 109, 5223–5228 (2012).
Google Scholar
Seth, P. P. et al. Sar by ms: discovery of a new class of RNA-binding small molecules for the hepatitis C virus: internal ribosome entry site IIA subdomain. J. Med. Chem. 48, 7099–7102 (2005).
Google Scholar
Wu, J. C., Chattree, G. & Ren, P. Automation of amoeba polarizable force field parameterization for small molecules. Theor. Chem. Acc. 131, 1–11 (2012).
Shi, Y. et al. Polarizable atomic multipole-based amoeba force field for proteins. J. Chem. Theory Comput. 9, 4046–4063 (2013).
Google Scholar
Yang, X., Liu, C., Kuo, Y.-A., Yeh, H.-C. & Ren, P. Computational study on the binding of mango-II RNA aptamer and fluorogen using the polarizable force field amoeba. Front. Mol. Biosci. 9, 946708 (2022).
Google Scholar
Lagardère, L. et al. Tinker-hp: a massively parallel molecular dynamics package for multiscale simulations of large complex systems with advanced point dipole polarizable force fields. Chem. Sci. 9, 956–972 (2018).
Google Scholar
Jolly, L.-H. et al. Raising the performance of the tinker-hp molecular modeling package [article v1.0]. Living J. Comput. Mol. Sci. 1, 10409 (2019).
Fiorin, G., Klein, M. L. & Hénin, J. Using collective variables to drive molecular dynamics simulations. Mol. Phys. 111, 3345–3362 (2013).
Google Scholar
Bonati, L., Rizzi, V. & Parrinello, M. Data-driven collective variables for enhanced sampling. J. Phys. Chem. Lett. 11, 2998–3004 (2020).
Google Scholar
Padroni, G., Patwardhan, N., Schapira, M. & Hargrove, A. Systematic analysis of the interactions driving small molecule–rna recognition. RSC Med. Chem. 11, 802–813 (2020).
Google Scholar
Chen, W. et al. Enhancing hit discovery in virtual screening through absolute protein–ligand binding free-energy calculations. J. Chem. Inf. Model. 63, 3171–3185 (2023).
Google Scholar
Parsons, J. et al. Conformational inhibition of the hepatitis C virus internal ribosome entry site RNA. Nat. Chem. Biol. 5, 823–825 (2009).
Google Scholar
Santiago-McRae, E., Ebrahimi, M., Sandberg, J. W., Brannigan, G. & Hénin, J. Computing absolute binding affinities by streamlined alchemical free energy perturbation (safep)[article v1. 0]. Living J. Comput. Mol. Sci. 5, 2067–2067 (2023).
Invernizzi, M. & Parrinello, M. Rethinking metadynamics: from bias potentials to probability distributions. J. Phys. Chem. Lett. 11, 2731–2736 (2020).
Google Scholar
Invernizzi, M. & Parrinello, M. Exploration vs convergence speed in adaptive-bias enhanced sampling. J. Chem. Theory Comput. 18, 3988–3996 (2022).
Google Scholar
Walker, B., Liu, C., Wait, E. & Ren, P. Automation of amoeba polarizable force field for small molecules: Poltype 2. J. Comput. Chem. 43, 1530–1542 (2022).
Google Scholar
Turney, J. M. et al. Psi4: an open-source ab initio electronic structure program. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2, 556–565 (2012).
Google Scholar
Stone, A. J. & Alderton, M. Distributed multipole analysis: methods and applications. Mol. Phys. 56, 1047–1064 (1985).
Google Scholar
Rackers, J. A. et al. Tinker 8: software tools for molecular design. J. Chem. theory Comput. 14, 5273–5289 (2018).
Google Scholar
Ren, P., Wu, C. & Ponder, J. W. Polarizable atomic multipole-based molecular mechanics for organic molecules. J. Chem. Theory Comput. 7, 3143–3161 (2011).
Google Scholar
Zhang, C., Bell, D., Harger, M. & Ren, P. Polarizable multipole-based force field for aromatic molecules and nucleobases. J. Chem. Theory Comput. 13, 666–678 (2017).
Google Scholar
Bannwarth, C. et al. Extended tight-binding quantum chemistry methods. Wiley Interdiscip. Rev. Comput. Mol. Sci. 11, e1493 (2021).
Google Scholar
Bannwarth, C., Ehlert, S. & Grimme, S. Gfn2-xtb-an accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions. J. Chem. Theory Comput. 15, 1652–1671 (2019).
Google Scholar
Chai, J.-D. & Head-Gordon, M. Long-range corrected hybrid density functionals with damped atom–atom dispersion corrections. Phys. Chem. Chem. Phys. 10, 6615–6620 (2008).
Google Scholar
Tuckerman, M., Berne, B. J. & Martyna, G. J. Reversible multiple time scale molecular dynamics. J. Chem. Phys. 97, 1990–2001 (1992).
Google Scholar
Bussi, G., Donadio, D. & Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 126, 014101 (2007).
Berendsen, H. J., Postma, J. V., Van Gunsteren, W. F., DiNola, A. & Haak, J. R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684–3690 (1984).
Google Scholar
Essmann, U. et al. A smooth particle mesh ewald method. J. Chem. Phys. 103, 8577–8593 (1995).
Google Scholar
Lagardère, L. et al. Scalable evaluation of polarization energy and associated forces in polarizable molecular dynamics: Ii. toward massively parallel computations using smooth particle mesh ewald. J. Chem. Theory Comput. 11, 2589–2599 (2015).
Google Scholar
Laury, M. L., Wang, Z., Gordon, A. S. & Ponder, J. W. Absolute binding free energies for the sampl6 cucurbit [8] uril host–guest challenge via the amoeba polarizable force field. J. Comput. Aided Mol. Des. 32, 1087–1095 (2018).
Google Scholar
Boresch, S., Tettinger, F., Leitgeb, M. & Karplus, M. Absolute binding free energies: a quantitative approach for their calculation. J. Phys. Chem. B 107, 9535–9551 (2003).
Google Scholar
Hénin, J., Lopes, L. J. & Fiorin, G. Human learning for molecular simulations: the collective variables dashboard in VMD. J. Chem. Theory Comput. 18, 1945–1956 (2022).
Google Scholar
Humphrey, W., Dalke, A. & Schulten, K. Vmd: visual molecular dynamics. J. Mol. Graph. 14, 33–38 (1996).
Google Scholar
Straatsma, T. P. & McCammon, J. A. Multiconfiguration thermodynamic integration. J. Chem. Phys. 95, 1175–1188 (1991).
Google Scholar
Zwanzig, R. W. High-temperature equation of state by a perturbation method. i. nonpolar gases. J. Chem. Phys. 22, 1420–1426 (1954).
Google Scholar
Jiang, W. & Roux, B. Free energy perturbation hamiltonian replica-exchange molecular dynamics (fep/h-remd) for absolute ligand binding free energy calculations. J. Chem. Theory Comput. 6, 2559–2565 (2010).
Google Scholar
Lyubartsev, A., Martsinovski, A., Shevkunov, S. & Vorontsov-Velyaminov, P. New approach to Monte Carlo calculation of the free energy: method of expanded ensembles. J. Chem. Phys. 96, 1776–1783 (1992).
Google Scholar
Thomas, J. R. & Hergenrother, P. J. Targeting RNA with small molecules. Chem. Rev. 108, 1171–1224 (2008).
Google Scholar
Far, S. et al. Bis-and tris-DNA intercalating porphyrins designed to target the major groove: Synthesis of acridylbis-arginyl-porphyrins, molecular modelling of their DNA complexes, and experimental tests. Eur. J. Org. Chem. 2004, 1781–1797 (2004).
Petrov, D., Perthold, J. W., Oostenbrink, C., de Groot, B. L. & Gapsys, V. Guidelines for free-energy calculations involving charge changes. J. Chem. Theory Comput. 20, 914–925 (2024).
Google Scholar
Barducci, A., Bonomi, M. & Parrinello, M. Metadynamics. WIREs Comput. Mol. Sci. 1, 826–843 (2011).
Google Scholar
Welling, M. Fisher Linear Discriminant Analysis. Tech. Rep., Dep. Comput. Sci. Univ. Toronto (2005).
Rizzi, V., Bonati, L., Ansari, N. & Parrinello, M. The role of water in host-guest interaction. Nat. Commun. 12, 93 (2021).
Google Scholar
Ansari, N., Rizzi, V., Carloni, P. & Parrinello, M. Water-triggered, irreversible conformational change of SARS-CoV-2 main protease on passing from the solid state to aqueous solution. J. Am. Chem. Soc. 143, 12930–12934 (2021).
Google Scholar
Ansari, N., Rizzi, V. & Parrinello, M. Water regulates the residence time of benzamidine in trypsin. Nat. Commun. 13, 5438 (2022).
Google Scholar
Bjelobrk, Z. et al. Naphthalene crystal shape prediction from molecular dynamics simulations. Cryst. Eng. Comm. 21, 3280–3288 (2019).
Google Scholar
Leontis, N. B., Stombaugh, J. & Westhof, E. The non–Watson–Crick base pairs and their associated isostericity matrices. Nucleic Acids Res. 30, 3497–3531 (2002).
Google Scholar