From physics-based intelligence to molecular design: linking mechanistic learning across scales

Jian Zhang , Paolo Alberto Lorenzini , Feng Ren , Xavier Daura

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MedScience ›› DOI: 10.1007/s11684-026-1263-6
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From physics-based intelligence to molecular design: linking mechanistic learning across scales
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Jian Zhang, Paolo Alberto Lorenzini, Feng Ren, Xavier Daura. From physics-based intelligence to molecular design: linking mechanistic learning across scales. MedScience DOI:10.1007/s11684-026-1263-6

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References

[1]

Skalic M , Sabbadin D , Sattarov B , Sciabola S , De Fabritiis G . From target to drug: generative modeling for the multimodal structure-based ligand design. Mol Pharm 2019; 16(10): 4282–4291

[2]

Zhang ZMin YZheng SLiu Q. Molecule generation for target protein binding with structural motifs. February 2, 2023. Accessed June 1, 2026. Available at the website of openreview. net/forum?id=Rq13idF0F73

[3]

Zhang ZLiu Q. Learning subpocket prototypes for generalizable structure-based drug design. arXiv 2023; doi: 10.48550/arXiv. 2305.13997

[4]

Zhung W , Kim H , Kim WY . 3D molecular generative framework for interaction-guided drug design. Nat Commun 2024; 15(1): 2688

[5]

Jiang Y , Zhang G , You J , Zhang H , Yao R , Xie H , Zhang L , Xia Z , Dai M , Wu Y , Li L , Yang S . PocketFlow is a data-and-knowledge-driven structure-based molecular generative model. Nat Mach Intell 2024; 6(3): 326–337

[6]

Chen Z , Peng B , Zhai T , Adu-Ampratwum D , Ning X . Generating 3D binding molecules using shape-conditioned diffusion models with guidance. Nat Mach Intell 2025; 7(5): 758–770

[7]

Peng J , Yu JL , Yang ZB , Chen YT , Wei SQ , Meng FB , Wang YG , Huang XT , Li GB . Pharmacophore-oriented 3D molecular generation toward efficient feature-customized drug discovery. Nat Comput Sci 2025; 5(10): 898–914

[8]

Li M , Song K , He J , Zhao M , You G , Zhong J , Zhao M , Li A , Chen Y , Li G , Kong Y , Wei J , Wang Z , Zhou J , Yang H , Ma S , Zhang H , Mélita IL , Lin W , Lu Y , Yu Z , Lu X , Zhao Y , Zhang J . Electron-density-informed effective and reliable de novo molecular design and optimization with ED2Mol. Nat Mach Intell 2025; 7(8): 1355–1368

[9]

van Gunsteren WF , Bakowies D , Baron R , Chandrasekhar I , Christen M , Daura X , Gee P , Geerke DP , Glättli A , Hünenberger PH , Kastenholz MA , Oostenbrink C , Schenk M , Trzesniak D , van der Vegt NFA , Yu HB . Biomolecular modeling: goals, problems, perspectives. Angew Chem Int Ed 2006; 45(25): 4064–4092

[10]

Chen M , Jiang X , Zhang L , Chen X , Wen Y , Gu Z , Li X , Zheng M . The emergence of machine learning force fields in drug design. Med Res Rev 2024; 44(3): 1147–1182

[11]

Peng X , Guo R , Guo F , Wang Z , Sun J , Guan J , Jia Y , Xu Y , Huang Y , Zhang M , Peng J , Wang X , Han C , Wang Z , Ma J. . Unified modeling of 3D molecular generation via atomic interactions with PocketXMol. Cell 2026; 189(7): 1904–1922.e28

[12]

Lam JH , Katritch V . Navigating structure-based drug discovery with emerging innovations in physics- and knowledge-based approaches. npj Drug Discov 2025; 2: 29

[13]

Wang T , He X , Li M , Li Y , Bi R , Wang Y , Cheng C , Shen X , Meng J , Zhang H , Liu H , Wang Z , Li S , Shao B , Liu TY . Ab initio characterization of protein molecular dynamics with AI2BMD. Nature 2024; 635(8040): 1019–1027

[14]

Lewis S , Hempel T , Jiménez-Luna J , Gastegger M , Xie Y , Foong AYK , Satorras VG , Abdin O , Veeling BS , Zaporozhets I , Chen Y , Yang S , Foster AE , Schneuing A , Nigam J , Barbero F , Stimper V , Campbell A , Yim J , Lienen M , Shi Y , Zheng S , Schulz H , Munir U , Sordillo R , Tomioka R , Clementi C , Noé F . Scalable emulation of protein equilibrium ensembles with generative deep learning. Science 2025; 389(6761): eadv9817

[15]

Jing B , Berger B , Jaakkola T . AlphaFold meets flow matching for generating protein ensembles. ;

[16]

Li S , Wang Y , Li M , Zhang J , Shao B , Zheng N , Tang J . F3low: frame-to-frame coarse-grained molecular dynamics with SE(3) guided flow matching. ;

[17]

Chen X , Wang K , Chen J , Wu C , Mao J , Song Y , Liu Y , Shao Z , Pu X . Integrative residue-intuitive machine learning and MD approach to unveil allosteric site and mechanism for β2AR. Nat Commun 2024; 15(1): 8130

[18]

Qiao Z , Nie W , Vahdat A , Miller TF III , Anandkumar A . State-specific protein–ligand complex structure prediction with a multiscale deep generative model. Nat Mach Intell 2024; 6(2): 195–208

[19]

del Alamo D , Sala D , Mchaourab HS , Meiler J . Sampling alternative conformational states of transporters and receptors with AlphaFold2. eLife 2022; 11: e75751

[20]

Monteiro da Silva G , Cui JY , Dalgarno DC , Lisi GP , Rubenstein BM . High-throughput prediction of protein conformational distributions with subsampled AlphaFold2. Nat Commun 2024; 15(1): 2464

[21]

Khokhar M , Keskin O , Gursoy A . DeepAllo: allosteric site prediction using protein language model (pLM) with multitask learning. Bioinformatics 2025; 41(6): btaf294

[22]

Zhu R , Wu C , Zha J , Lu S , Zhang J . Decoding allosteric landscapes: computational methodologies for enzyme modulation and drug discovery. RSC Chem Biol 2025; 6(4): 539–554

[23]

Jin Y , Huang Q , Song Z , Zheng M , Teng D , Shi Q . P2DFlow: a protein ensemble generative model with SE(3) flow matching. J Chem Theory Comput 2025; 21(6): 3288–3296

[24]

Maity D , Qiao B . AlloBench: a data set pipeline for the development and benchmarking of allosteric site prediction tools. ACS Omega 2025; 10(17): 17973–17982

[25]

Huang W , Lu S , Huang Z , Liu X , Mou L , Luo Y , Zhao Y , Liu Y , Chen Z , Hou T , Zhang J . Allosite: a method for predicting allosteric sites. Bioinformatics 2013; 29(18): 2357–2359

[26]

Gazizov A , Lian A , Goverde C , Mou J , Ovchinnikov S , Polizzi NF . AF2BIND: predicting small-molecule binding sites using the pair representation of AlphaFold2. Nat Methods 2026; 23(3): 626–635

[27]

Ni D , Chai Z , Wang Y , Li M , Yu Z , Liu Y , Lu S , Zhang J . Along the allostery stream: recent advances in computational methods for allosteric drug discovery. Wiley Interdiscip Rev Comput Mol Sci 2022; 12(4): e1585

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