9 research outputs found
CORE: Automatic Molecule Optimization Using Copy & Refine Strategy
Molecule optimization is about generating molecule with more desirable
properties based on an input molecule . The state-of-the-art approaches
partition the molecules into a large set of substructures and grow the new
molecule structure by iteratively predicting which substructure from to
add. However, since the set of available substructures is large, such an
iterative prediction task is often inaccurate especially for substructures that
are infrequent in the training data. To address this challenge, we propose a
new generating strategy called "Copy & Refine" (CORE), where at each step the
generator first decides whether to copy an existing substructure from input
or to generate a new substructure, then the most promising substructure will be
added to the new molecule. Combining together with scaffolding tree generation
and adversarial training, CORE can significantly improve several latest
molecule optimization methods in various measures including drug likeness
(QED), dopamine receptor (DRD2) and penalized LogP. We tested CORE and
baselines using the ZINC database and CORE obtained up to 11% and 21%
relatively improvement over the baselines on success rate on the complete test
set and the subset with infrequent substructures, respectively.Comment: Accepted by AAAI 202
Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization
Molecular optimization is a fundamental goal in the chemical sciences and is
of central interest to drug and material design. In recent years, significant
progress has been made in solving challenging problems across various aspects
of computational molecular optimizations, emphasizing high validity, diversity,
and, most recently, synthesizability. Despite this progress, many papers report
results on trivial or self-designed tasks, bringing additional challenges to
directly assessing the performance of new methods. Moreover, the sample
efficiency of the optimization--the number of molecules evaluated by the
oracle--is rarely discussed, despite being an essential consideration for
realistic discovery applications.
To fill this gap, we have created an open-source benchmark for practical
molecular optimization, PMO, to facilitate the transparent and reproducible
evaluation of algorithmic advances in molecular optimization. This paper
thoroughly investigates the performance of 25 molecular design algorithms on 23
tasks with a particular focus on sample efficiency. Our results show that most
"state-of-the-art" methods fail to outperform their predecessors under a
limited oracle budget allowing 10K queries and that no existing algorithm can
efficiently solve certain molecular optimization problems in this setting. We
analyze the influence of the optimization algorithm choices, molecular assembly
strategies, and oracle landscapes on the optimization performance to inform
future algorithm development and benchmarking. PMO provides a standardized
experimental setup to comprehensively evaluate and compare new molecule
optimization methods with existing ones. All code can be found at
https://github.com/wenhao-gao/mol_opt
Quasi-Newton Hamiltonian Monte Carlo
Abstract The Hamiltonian Monte Carlo (HMC) method has become significantly popular in recent years. It is the state-of-the-art MCMC sampler due to its more efficient exploration to the parameter space than the standard random-walk based proposal. The key idea behind HMC is that it makes use of first-order gradient information about the target distribution. In this paper, we propose a novel dynamics using second-order geometric information about the desired distribution. The second-order information is estimated by using a quasi-Newton method (say, the BFGS method), so it does not bring heavy computational burden. Moreover, our theoretical analysis guarantees that this dynamics remains the target distribution invariant. As a result, the proposed quasiNewton Hamiltonian Monte Carlo (QNHMC) algorithm traverses the parameter space more efficiently than the standard HMC and produces a less correlated series of samples. Finally, empirical evaluation on simulated data verifies the effectiveness and efficiency of our approach. We also conduct applications of QNHMC in Bayesian logistic regression and online Bayesian matrix factorization problems
MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization
Molecule optimization is a fundamental task for accelerating drug discovery,
with the goal of generating new valid molecules that maximize multiple drug
properties while maintaining similarity to the input molecule. Existing
generative models and reinforcement learning approaches made initial success,
but still face difficulties in simultaneously optimizing multiple drug
properties. To address such challenges, we propose the MultI-constraint
MOlecule SAmpling (MIMOSA) approach, a sampling framework to use input molecule
as an initial guess and sample molecules from the target distribution. MIMOSA
first pretrains two property agnostic graph neural networks (GNNs) for molecule
topology and substructure-type prediction, where a substructure can be either
atom or single ring. For each iteration, MIMOSA uses the GNNs' prediction and
employs three basic substructure operations (add, replace, delete) to generate
new molecules and associated weights. The weights can encode multiple
constraints including similarity and drug property constraints, upon which we
select promising molecules for next iteration. MIMOSA enables flexible encoding
of multiple property- and similarity-constraints and can efficiently generate
new molecules that satisfy various property constraints and achieved up to
49.6% relative improvement over the best baseline in terms of success rate.Comment: Accepted by AAAI 202
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of
discoveries in natural sciences. Today, AI has started to advance natural
sciences by improving, accelerating, and enabling our understanding of natural
phenomena at a wide range of spatial and temporal scales, giving rise to a new
area of research known as AI for science (AI4Science). Being an emerging
research paradigm, AI4Science is unique in that it is an enormous and highly
interdisciplinary area. Thus, a unified and technical treatment of this field
is needed yet challenging. This work aims to provide a technically thorough
account of a subarea of AI4Science; namely, AI for quantum, atomistic, and
continuum systems. These areas aim at understanding the physical world from the
subatomic (wavefunctions and electron density), atomic (molecules, proteins,
materials, and interactions), to macro (fluids, climate, and subsurface) scales
and form an important subarea of AI4Science. A unique advantage of focusing on
these areas is that they largely share a common set of challenges, thereby
allowing a unified and foundational treatment. A key common challenge is how to
capture physics first principles, especially symmetries, in natural systems by
deep learning methods. We provide an in-depth yet intuitive account of
techniques to achieve equivariance to symmetry transformations. We also discuss
other common technical challenges, including explainability,
out-of-distribution generalization, knowledge transfer with foundation and
large language models, and uncertainty quantification. To facilitate learning
and education, we provide categorized lists of resources that we found to be
useful. We strive to be thorough and unified and hope this initial effort may
trigger more community interests and efforts to further advance AI4Science