Robert Frank

• Frank, Robert; Widrich, Michael; Akbar, Rahmad; Klambauer, Günter; Sandve, Geir Kjetil Ferkingstad & Robert, Philippe [Vis alle 7 forfattere av denne artikkelen] (2026). Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data. PLoS Computational Biology. ISSN 1553-734X. 22(9), s. 1–28. doi: 10.1371/journal.pcbi.1014805. Fulltekst i vitenarkiv
• Robert, Philippe Paul Auguste; Akbar, Rahmad; Frank, Robert; Pavlović, Milena; Widrich, Michael & Snapkov, Igor [Vis alle 25 forfattere av denne artikkelen] (2022). Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction. Nature Computational Science. 2(12), s. 845–865. doi: 10.1038/s43588-022-00372-4. Fulltekst i vitenarkiv
• Akbar, Rahmad; Bashour, Habib; Rawat, Puneet; Robert, Philippe Paul Auguste; Smorodina, Eva & Cotet, Tudor-Stefan [Vis alle 15 forfattere av denne artikkelen] (2022). Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. mAbs. ISSN 1942-0862. 14:e2008790(1), s. 1–35. doi: 10.1080/19420862.2021.2008790. Fulltekst i vitenarkiv Vis sammendrag

  Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.

• Akbar, Rahmad; Robert, Philippe Paul Auguste; Weber, Cédric R.; Widrich, Michael; Frank, Robert & Pavlovic, Milena [Vis alle 19 forfattere av denne artikkelen] (2022). In silico proof of principle of machine learning-based antibody design at unconstrained scale. mAbs. ISSN 1942-0862. 14:e2031482(1), s. 1–18. doi: 10.1080/19420862.2022.2031482. Fulltekst i vitenarkiv Vis sammendrag

  Generative machine learning (ML) has been postulated to become a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody-binding parameters. The simulation framework enables the computation of synthetic antibody-antigen 3D-structures, and it functions as an oracle for unrestricted prospective evaluation and benchmarking of antibody design parameters of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (one dimensional: 1D) data can be used to design conformational (three dimensional: 3D) epitope-specific antibodies, matching, or exceeding the training dataset in affinity and developability parameter value variety. Furthermore, we established a lower threshold of sequence diversity necessary for high-accuracy generative antibody ML and demonstrated that this lower threshold also holds on experimental real-world data. Finally, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design.

• Pavlović, Milena; Scheffer, Lonneke; Motwani, Keshav; Kanduri, Chakravarthi; Kompova, Radmila & Vazov, Nikolay Aleksandrov [Vis alle 42 forfattere av denne artikkelen] (2021). The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nature Machine Intelligence. 3(11), s. 936–944. doi: 10.1038/s42256-021-00413-z. Fulltekst i vitenarkiv

Se alle arbeider i NVA