Peter Fedichev is a physicist and biotechnologist. He worked on ultracold quantum systems before moving into computational drug design and later longevity research.
He is the co-founder and chief executive officer of Gero, a biotechnology company based in Singapore that applies physics-based and machine-learning methods to ageing and disease research.
Education and early career Fedichev received an M.Sc. in theoretical physics from the Moscow Institute of Physics and Technology while conducting research at the Kurchatov Institute. In 1994 he joined the University of Amsterdam and the AMOLF institute, completing a Ph.D. cum laude in theoretical physics. He later worked at the University of Innsbruck on condensed matter and quantum-gas systems.
Research
Quantum physics research Following his doctoral studies at the University of Amsterdam and AMOLF, Fedichev worked in the field of ultracold atomic gases, Bose-Einstein condensates, quantum information, and many-body quantum systems. During the late 1990s and early 2000s he collaborated with physicists including Peter Zoller, J. Ignacio Cirac, Jan von Delft, Uwe R. Fischer, and Andrew Daley on problems in quantum simulation, strongly correlated quantum matter, and analogue models of gravitational phenomena. His research contributed to several areas of quantum physics, including anyonic excitations in ultracold gases, spin-charge separation, quantum information processing, and analogue gravity. Several of these publications became highly cited within the quantum-gas and quantum-simulation communities. His paper "Influence of Nearly Resonant Light on the Scattering Length in Low-Temperature Atomic Gases" is among the early theoretical works on optical control of atomic interactions in ultracold gases. Some of Fedichev's collaborators, including Peter Zoller and J. Ignacio Cirac, later received the 2022 Wolf Prize in Physics for foundational contributions to quantum information science and quantum simulation. Their work has frequently been discussed as laying foundations for modern quantum computing and quantum simulation technologies.
Computational drug discovery and molecular modeling In the early 2000s, Fedichev moved from theoretical physics into computational chemistry and drug discovery and co-founded Quantum Pharmaceuticals, an early company exploring computational drug design. This work applied physics-based computational modeling to molecular simulation, biomolecular electrostatics, virtual screening, and structure-based drug design, including antiviral and metabolism-related drug discovery programs. Fedichev and collaborators reported the discovery of small-molecule antiviral compounds targeting influenza A nucleoprotein and the HIV-1 matrix protein. This work resulted in a series of intellectual property filings. Fedichev is listed as an inventor on U.S. Patent 9,610,264, "Compounds for the Treatment and Prevention of Retroviral Infections", granted in 2017. The patent covers a class of small-molecule compounds intended for the treatment and prevention of retroviral infections, including HIV infection. Fedichev also co-authored studies on modulation of phosphofructokinase enzymes involved in glycolysis, work that later contributed to therapeutic programs targeting cancer metabolism, neurodegeneration, and aging-related diseases. Fedichev's research also included the application of computational chemistry and structure-based drug design to antibacterial drug discovery. In collaboration with researchers at New York University, he participated in efforts to identify inhibitors of bacterial hydrogen sulfide (H₂S) biosynthesis, a pathway implicated in antibiotic resistance, antibiotic tolerance, persister-cell formation, and biofilm survival. This work contributed to the identification of allosteric inhibitors of bacterial cystathionine γ-lyase (bCSE), an enzyme responsible for endogenous H₂S production in several pathogenic bacteria. The inhibitors were shown to potentiate multiple classes of bactericidal antibiotics, reduce bacterial persistence, disrupt biofilm formation, and improve antibiotic efficacy in animal infection models.
Aging research and drug discovery Since 2015, Fedichev has led Gero.ai, whose studies use large health datasets and mathematical models to analyse ageing dynamics, and he has been the principal proponent of a physics-based, quantitative approach to aging that treats the organism as a dynamical system near a stability boundary and describes aging using concepts from statistical physics, [[non-equilibrium thermodynamics]], and the theory of stochastic processes. A central premise is that aging is a system-level process, the progressive loss of physiological resilience, rather than the sum of individual diseases; Fedichev has argued that targeting this upstream process could delay multiple age-related conditions at once. As early as 2012, before his first formal publications in the area, Fedichev was publicly discussing a possible connection between phase transitions in [[gene regulatory network]]s and the dynamics of aging, suggesting in a blog post that the stability of such networks near a critical point might be related to slow or "negligible" aging. He developed these ideas into a formal model in 2015. Key elements of the framework, developed by Fedichev and his collaborators at Gero with Fedichev as senior author, include:
Mortality from network dynamics (2015). Fedichev and colleagues argued analytically that the Gompertz mortality law can emerge from the critical dynamics of gene regulatory networks operating near a stability boundary, linking network-level dynamics to demographic mortality patterns without invoking programmed aging. It was among the earliest attempts to derive a demographic law of mortality from underlying system dynamics.
Strehler–Mildvan correlation (2017). Fedichev and colleagues showed that the long-cited Strehler–Mildvan correlation may arise as a degenerate artifact of fitting the Gompertz model rather than reflecting an independent biological mechanism, implying that some established empirical regularities in aging research may be statistical rather than causal.
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