About

Patrick S. Doyle is the Robert T Haslam (1911) Professor in Chemical Engineering at MIT. His research focuses on chemical engineering, with particular emphasis on areas such as energy, environment and sustainability, and materials. As a faculty member, he contributes to the department's academic and research missions, engaging in innovative work that advances the field of chemical engineering.

Research topics

• Computer Science
• Nanotechnology
• Materials science
• Physics
• Mechanics
• Engineering
• Chromatography
• Process engineering
• Biology
• Chemistry
• Biochemistry
• Mechanical engineering
• Computational biology
• Composite material

Selected publications

• Structured Emulsions in Switchable Soft-Matter for Formulating Stable Pharmaceutical Microcrystal Suspensions

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• MicroRNA spatial profiling for assessing drug efficacy in <i>BRCA1</i> -related triple-negative breast tumors

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-01-12

  articleSenior authorCorresponding

  Abstract text BRCA1/2 -mutated breast cancers exhibit homologous recombination deficiency (HRD), making them initially sensitive to poly(ADP-ribose) polymerase (PARP) inhibitors. However, 40-70% of patients develop resistance, necessitating combination strategies and predictive biomarkers. We first investigated approaches to overcome PARP resistance and then explored spatial microRNA (miRNA) profiling as a prognostic tool. Using the K14-Cre Brca1 f/f Trp53 f/f model with tumors that acquired PARP resistance, we evaluated PARP inhibitor combinations with either PI3K inhibition or Poly(I:C) in vivo . Both combinations improved antitumor activity compared to PARP inhibition alone. Next, to predict resistance we applied a sensitive assay that quantifies and spatially profiles miRNA expression in situ onto FFPE sections from tumors treated for 10 days using nanoliter well arrays with functionalized hydrogel posts. We developed a spatial miRNA analysis framework integrating latent Dirichlet allocation (LDA) and principal component analysis (PCA) to develop “topics” that stratify early tumors as either PARP inhibitor-sensitive or - resistant and distinguish their treatment regimens. We also incorporated immune architecture using Structural Similarity Index Measure (SSIM) maps that revealed co-localization of immune infiltration and miRNA topics. This integrative approach highlights how miRNA-based spatial analysis can predict PARP inhibitor resistance and provide a promising biomarker to inform therapeutic strategies for BRCA1/2- related breast cancers.

  PublisherDOI

• Interpretable Deep Learning for Single-Molecule Nanopore Fingerprinting Using Physics-Guided Preprocessing

  ACS Sensors · 2026-02-20

  articleSenior authorCorresponding

  Rapid and robust molecular fingerprinting is critical in biomanufacturing, diagnostics, and environmental monitoring. Nanopore sensing provides single-molecule readouts as transient ionic current pulses; however, conventional analyses depend on handcrafted features that miss informative structural information. We present an interpretable machine learning framework that operates directly on raw pulses, pairing a physics-guided time-frequency transform with a compact neural classifier and feature-attribution maps. We also include conventional feature-based SVMs and a 1D classifier trained on raw pulses as baselines. On two self-assembled DNA nanostructures of similar size but distinct geometry, for which standard pulse features overlap, the method achieves high accuracy and yields physically consistent attributions that highlight discriminative signal motifs. A matched control without the time-frequency transform clarifies when learned filters suffice versus when physics-guided preprocessing improves reliability, leading to a practical "custom-filter" design principle. The workflow is modular, lightweight, and applicable to pulse-based sensing platforms, including virus and exosome analysis, electrochemical monitoring, and industrial fault detection. By combining accuracy with transparency, it lays the groundwork for deployable sensing platforms in regulated, mission-critical settings.

  PublisherDOI

• High-Throughput Generation of Droplet-Templated Amorphous Solid Dispersions Using Continuous Antisolvent Extraction

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Size-Controlled Templating of Stable Drug Nanoparticles from Nanoemulsion Precursors for Versatile Nanoformulation

  Chemistry of Materials · 2025-12-31

  articleSenior authorCorresponding

  Nanosizing drug particles has emerged as a successful approach to enable the oral bioavailability of lipophilic small molecule drugs. Scalable “bottom-up” methods have been developed to overcome the limitations and resource-intensiveness of traditional “top-down” nanoparticle production. However, bottom-up approaches are still limited in their applicability across drug chemistries, their ability to control particle size distributions, and the long-term stability of the generated nanoparticles. Here, we overcome these limitations by applying a versatile nanoemulsion templating approach to generate drug nanoparticle formulations inside a hydrogel thin film. By using different dispersed phase solvents, we formulate four chemically diverse drug molecules. Nanoparticle size is precisely tuned by controlling precursor nanoemulsion droplet size, enabling customizable formulations between 100–1000 nm. The resulting nanoparticles retain stable size distributions and solid states for at least six months at room temperature. We demonstrate the in vitro bioavailability enhancement of our nanoformulations through dramatically faster dissolution, increased apparent thermodynamic solubility, and enhanced permeability across Caco-2 cell monolayers. Notably, we quantitatively measure the solubility enhancement as a function of nanoparticle size and report a rare validation of the Ostwald–Freundlich equation. The thin-film form factor of our nanoformulations could enable applications in buccal delivery, oral delivery for pediatric, elderly, or dysphagic patients, and “suspensions-on-demand” for stable storage of point-of-care nanoparticle suspensions. Together, this work introduces a general, tunable, and shelf-stable platform for rapid fit-for-purpose pharmaceutical nanoformulations.

  PublisherDOI

• Surface-Mediated Self-Assembly of Kinetoplast DNA: Depletion-Driven Dimer Formation and Quasi-2D Dynamics

  Langmuir · 2025-11-14

  articleSenior authorCorresponding

  Depletion interactions play a crucial role in the assembly and dynamics of colloidal systems in polymer-rich environments. In this study, we investigate the behavior of asymmetric, soft, colloidal kinetoplast DNA (kDNA) in the presence of linear polymers (linear DNA), focusing on their surface accumulation, orientation, diffusion, and dimer formation. We observe that the kDNAs preferentially migrate to a solid surface with a preferred orientation due to depletion interactions with the substrate, a phenomenon absent in polymer-free conditions. Over time, the kDNAs adopt a stable orientation at the surface, and the orientation of individual kDNAs on the surface adopts a polar order. By analyzing kDNA diffusion both in bulk and on the surface, we find that surface-bound kDNAs exhibit prolonged confinement within the field of view, while bulk kDNAs rapidly diffuse out of view. Additionally, we observe the formation of dimers as kDNAs encounter each other on the surface, driven by depletion forces. The kDNAs within the dimers are able to rotate relative to each other and deform to maximize their interaction energy. Fluorescent labeling of both the polymers and kDNAs reveals polymer exclusion from the kDNA-surface and inter-kDNA regions, confirming depletion-driven attraction. We quantified the depletion attraction by measuring the excluded volume between dimerized kDNAs using confocal fluorescence microscopy. These findings provide insights into depletion-mediated interactions in soft, asymmetric colloids and further establish kDNA as a model system for studying the colloidal behavior of catenated soft particles.

  PublisherDOI

• Organic Solubility Prediction at the Limit of Aleatoric Uncertainty

  ChemRxiv · 2025-05-09 · 1 citations

  preprintOpen access

  Small molecule solubility is a critically important property which affects the efficiency, environmental impact, and phase behavior of synthetic processes. Experimental determination of solubility is a time- and resource-intensive process and existing methods for in silico estimation of solubility are limited by their generality, speed, and accuracy. This work presents two models derived from the fastprop and chemprop architectures and trained on BigSolDB which are capable of predicting solubility at arbitrary temperatures for any small molecule in organic solvent. Both extrapolate to unseen solutes 2-3 times more accurately than the current state-of-the-art model and we demonstrate that they are approaching the aleatoric limit (0.5-1 logS), suggesting that further improvements in prediction accuracy require more accurate datasets. These models, collectively referred to as fastsolv, are open source, freely accessible via a Python package and web interface, highly reproducible, and up to 50 times faster than the next best alternative.

  PublisherOA PDFDOI

• Phase behavior of catenated-linear DNA mixtures

  Soft Matter · 2025-01-01 · 1 citations

  articleOpen accessSenior authorCorresponding

  Understanding the phase behavior of multicomponent systems is crucial in condensed matter physics, both for practical applications and fundamental exploration. Regardless of chemical composition, topology stands out as a crucial parameter in this context. We studied herein the phase behavior of a 2D catenated network of DNA rings called a kinetoplast in the presence of linear DNA. We examine the system at a fixed kinetoplast DNA concentration and linear DNA size, while varying the concentration of linear DNA. The mixing of circular DNA with linear DNA is reported to lead to the isotropic phase of the mixtures, however, catenated DNA rings (the assembly of circular DNA) lead to the phase separation in the presence of linear DNA. This distinction highlights the profound influence of topology on the phase behavior of polymer blends. The phase-separated aggregates of kinetoplasts exhibit a fractal nature, with the fractal dimension indicating the dominance of the diffusion-limited mechanism in the aggregation process. Although the structure of these aggregates is robust, significant thermal fluctuations in size and shape occur at various length scales. The understanding of the bulk phase behavior of the catenated DNA network provides crucial insights in designing the catenated-linear polymer composites.

  PublisherDOI

• Data-driven organic solubility prediction at the limit of aleatoric uncertainty

  Nature Communications · 2025-08-19 · 18 citations

  articleOpen access

  Abstract Small molecule solubility is a critically important property which affects the efficiency, environmental impact, and phase behavior of synthetic processes. Experimental determination of solubility is a time- and resource-intensive process and existing methods for in silico estimation of solubility are limited by their generality, speed, and accuracy. This work presents two models derived from the FASTPROP and CHEMPROP architectures and trained on BigSolDB which are capable of predicting solubility at arbitrary temperatures for a wide range of small molecules in organic solvent. Both extrapolate to unseen solutes 2–3 times more accurately than the current state-of-the-art model and we demonstrate that they are approaching the aleatoric limit (0.5–1 $$\log S$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>log</mml:mi> <mml:mi>S</mml:mi> </mml:math> ) of available test data, suggesting that further improvements in prediction accuracy require more accurate datasets. The FASTPROP-derived model (called FASTSOLV) and the CHEMPROP-based model are open source, freely accessible via a Python package and web interface, highly reproducible, and up to 2 orders of magnitude faster than current alternatives.

  PublisherOA PDFDOI

• Alginate exopolymer significantly modulates the viscoelastic properties and resilience of bacterial biofilms

  npj Biofilms and Microbiomes · 2025-06-09 · 4 citations

  articleOpen access

  Biofilms are viscoelastic gels with a cross-linked network of biopolymers forming an extracellular matrix that protects bacteria from most antimicrobial treatments. This study examines the physical role of the matrix in preventing recolonisation using a mucoid Pseudomonas aeruginosa (P. aeruginosa ΔmucA) and isogenic wild-type Pseudomonas aeruginosa PAO1. We investigated the recolonisation of pre-formed live biofilms and the residual matrix left behind after bacterial eradication with N-acetyl cysteine (NAC). P. aeruginosa ΔmucA, which overproduces alginate, prevented recolonisation through swelling and increased elastic modulus. In contrast, the wild-type P. aeruginosa biofilm matrix exhibited minimal swelling and decreased elasticity, suggesting crosslink breakage. These observations align with polymer physics theories where alginate's polyelectrolyte nature drives swelling through the Donnan effect, enhancing matrix stability. Meanwhile, the Psl-rich wild-type matrix limited swelling but showed reduced mechanical stability. This study underscores the critical role of matrix composition in biofilm mechanics, influencing bacterial protection regardless of viability.

  PublisherOA PDFDOI

Recent grants

• Polymer Dynamics of Knotted DNA

  NSF · $300k · 2016–2019

• DNA Polymer Dynamics in Nanoconfinement

  NSF · $300k · 2009–2013

• NIH Grant R21EB008814

  NIH · $423k · 2012

• Microfluidic Integrative Circulating miRNA Profiling for Cancer Diagnosis

  NIH · $231k · 2017–2019

• CAREER: Dynamics of Polymer Collisions

  NSF · $400k · 2003–2009

Frequent coauthors

• Johan R. C. van der Maarel

  National University of Singapore

  61 shared

• Liang Dai

  China XD Group (China)

  52 shared

• T. Alan Hatton

  Massachusetts Institute of Technology

  45 shared

• Saif A. Khan

  University of Ha'il

  42 shared

• Staffan Kjelleberg

  Singapore Centre for Environmental Life Sciences Engineering

  41 shared

• Stuart A. Rice

  Agriculture and Food

  41 shared

• Mehmet Toner

  Harvard University

  41 shared

• Ki Wan Bong

  Korea University

  40 shared

Labs

• MIT ChemEPI

Education

• Ph.D., Chemical Engineering

  Massachusetts Institute of Technology

  1990

• M.S., Chemical Engineering

  Massachusetts Institute of Technology

  1986

• B.S., Chemical Engineering

  University of California, Berkeley

  1984

Awards & honors

• AIChE's Alpha Chi Sigma Award for Chemical Engineering Resea…
• Singapore Research Professorship (2021)
• J-WAFS Seed Grant (2019)
• Singapore Research Professorship (2016)
• Michael Mohr Outstanding Faculty Award (2013 & 2014)