The scientific foundation behind our programs — key papers from the Jorgensen group spanning four decades of computational chemistry.
Comparison of Simple Potential Functions for Simulating Liquid Water
Journal of Chemical Physics · 1983 · Vol. 79, p. 926
Introduced the TIP3P and TIP4P water models — among the most widely used molecular dynamics water potentials in history. Defined a framework for parameterising intermolecular potential functions from structural and thermodynamic data on pure liquids. This paper has been cited over 40,000 times and underpins the majority of biomolecular simulations performed worldwide.
The OPLS Potential Functions for Proteins. Energy Minimizations for Crystals of Cyclic Peptides and Crambin
Journal of the American Chemical Society · 1988 · Vol. 110, p. 1657
The original OPLS force field for proteins — Optimized Potentials for Liquid Simulations. Derived by fitting to thermodynamic and structural data for organic liquids and extended to amino acid residues. Demonstrated accurate crystal structure reproduction for cyclic peptides and the protein crambin. The foundation of the OPLS family of force fields still in active use today.
Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids
Journal of the American Chemical Society · 1996 · Vol. 118, p. 11225
The landmark paper introducing OPLS-AA — the all-atom version of the OPLS force field, extending coverage to a broad range of organic functional groups. Validated against heats of vaporisation, densities, and conformational properties of 34 organic liquids. OPLS-AA became the industry standard for organic and pharmaceutical molecular simulations and remains one of the most-cited force fields in computational chemistry.
Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on Peptides
Journal of Physical Chemistry B · 2001 · Vol. 105, p. 6474
A systematic reparametrization of OPLS-AA torsional parameters for proteins using high-level quantum chemical calculations as benchmarks. Improved reproduction of peptide backbone conformations and side-chain rotameric preferences. This work established the procedure of using quantum mechanics to validate and correct molecular mechanics parameters — a methodology now standard in force field development.
Optimized Intermolecular Potential Functions for Liquid Hydrocarbons
Journal of the American Chemical Society · 1984 · Vol. 106, p. 6638
Extended the OPLS force field to liquid hydrocarbons using Monte Carlo statistical mechanics simulations via the BOSS program. Derived transferable united-atom potential functions for alkanes, alkenes, and aromatic compounds by optimising against thermodynamic properties of pure liquids. Established the OPLS-UA (united-atom) paradigm for efficient simulation of organic systems, used as a core methodology in BOSS to this day.
Free Energies in Solution from Monte Carlo Simulations
Accounts of Chemical Research · 1989 · Vol. 22, p. 184
A foundational review of Monte Carlo methodology for computing free energies of solvation, conformational equilibria, and chemical reactions in solution. Surveys the statistical perturbation theory framework implemented in BOSS and demonstrates its application to a range of chemical systems. Established the conceptual and practical basis for free energy calculations in solution that underpins all of the Foundation's simulation programs.
Molecular Modeling of Organic and Biomolecular Systems Using BOSS and MCPRO
Journal of Computational Chemistry · 2005 · Vol. 26, p. 1689
The primary methodological reference for the BOSS and MCPRO programs. Describes the Monte Carlo simulation capabilities, force field options, free energy perturbation protocols, and graphical interfaces available in both programs. Covers conformational searching, solvation calculations, and protein-ligand binding simulations. The definitive citation for researchers using BOSS or MCPRO in their work.
Monte Carlo Simulation of Differences in Free Energies of Hydration
Journal of Chemical Physics · 1985 · Vol. 83, p. 3050
The landmark paper introducing statistical perturbation theory to Monte Carlo simulations for computing relative free energies of hydration. Demonstrated that free energy differences between structurally similar solutes — such as methane and methanol — could be computed accurately by gradually mutating one molecule into another during simulation. This paper established the free energy perturbation (FEP) methodology that is central to modern computational drug discovery.
Perspective on Free-Energy Perturbation Calculations for Chemical Equilibria
Journal of Chemical Theory and Computation · 2008 · Vol. 4, p. 869
A critical perspective on the state of FEP methodology after more than two decades of development, examining sources of error, convergence behaviour, and best practices for computing chemical equilibria in solution. Reviews applications spanning solvation thermodynamics, conformational preferences, tautomeric equilibria, and protein-ligand binding. Essential reading for practitioners using the FEP capabilities of BOSS and MCPRO.
Prediction of Drug Solubility from Structure
Advanced Drug Delivery Reviews · 2002 · Vol. 54, p. 355
A comprehensive review of computational approaches to predicting aqueous solubility of drug candidates — one of the key ADME properties that determines whether a compound can become a viable drug. Evaluates methods ranging from empirical models to free energy perturbation calculations performed with BOSS and MCPRO. Demonstrates that Monte Carlo FEP yields solubility predictions accurate enough to guide lead optimisation, providing an early framework for the BOMB-based drug discovery pipeline.
The Many Roles of Computation in Drug Discovery
Science · 2004 · Vol. 303, p. 1813
A landmark review in Science surveying how computational chemistry — and the Jorgensen group's programs in particular — contributes at every stage of the drug discovery process: target identification, virtual screening with BOMB, lead optimisation via FEP with MCPRO, and ADME property prediction. Articulates the vision of computation as a primary driver rather than a supporting tool in medicinal chemistry, a view that has proven increasingly correct in the two decades since publication.
Efficient Drug Lead Discovery and Optimization
Accounts of Chemical Research · 2009 · Vol. 42, p. 724
Describes the complete computational drug discovery pipeline developed in the Jorgensen group, integrating BOMB for virtual screening, MCPRO for FEP-based binding affinity refinement, and property prediction tools for ADME optimisation. Presents case studies in HIV reverse transcriptase inhibitors and other targets where the pipeline has yielded potent, selective lead compounds. The definitive overview of how BOSS, MCPRO, and BOMB work together in practice for drug discovery campaigns.