Biology subjects

Vig, L.

Publications and source records attributed to Vig, L..

2 recordsLinked to original sources

bioRxiv2023

Generating Novel Leads for Drug Discovery using LLMs with Logical Feedback

Large Language Models (LLMs) can be used as repositories of biological and chemical information to generate pharmacological lead compounds. However, for LLMs to focus on specific drug targets typically require experimentation with progressively more refined prompts. Results thus become dependent not just on what is known about the target, but also on what is known about the prompt-engineering. In this paper, we separate the prompt into domain-constraints that can be written in a standard logical form, and a simple text-based query. We investigate whether LLMs can be guided, not by refining prompts manually, but by refining the the logical component automatically, keeping the query unchanged. We describe an iterative procedure LMLF ("Language Models with Logical Feedback") in which the constraints are progressively refined using a logical notion of generalisation. On any iteration, newly generated instances are verified against the constraint, providing "logical-feedback" for the next iterations refinement of the constraints. We evaluate LMLF using two well-known targets (inhibition of the Janus Kinase 2; and Dopamine Receptor D2); and two different LLMs (GPT-3 and PaLM). We show that LMLF, starting with the same logical constraints and query text, can guide both LLMs to generate potential leads. We find: (a) Binding affinities of LMLF-generated molecules are skewed towards higher binding affinities than those from existing baselines; LMLF results in generating molecules that are skewed towards higher binding affinities than without logical feedback; (c) Assessment by a computational chemist suggests that LMLF generated compounds may be novel inhibitors. These findings suggest that LLMs with logical feedback may provide a mechanism for generating new leads without requiring the domain-specialist to acquire sophisticated skills in prompt-engineering.

bioinformatics↗

bioRxiv2021

Using Domain-Knowledge to Assist Lead Discovery in Early-Stage Drug Design

We are interested in generating new small molecules which could act as inhibitors of a biological target, when there is limited prior information on target-specific inhibitors. This form of drug-design is assuming increasing importance with the advent of new disease threats for which known chemicals only provide limited information about target inhibition. In this paper, we propose the combined use of deep neural networks and Inductive Logic Programming (ILP) that allows the use of symbolic domain-knowledge (B) to explore the large space of possible molecules. Assuming molecules and their activities to be instances of random variables X and Y, the problem is to draw instances from the conditional distribution of X, given Y, B (DX|Y,B). We decompose this into the constituent parts of obtaining the distributions DX|B and DY|X,B, and describe the design and implementation of models to approximate the distributions. The design consists of generators (to approximate DX|B and DX|Y,B) and a discriminator (to approximate DY|X,B). We investigate our approach using the well-studied problem of inhibitors for the Janus kinase (JAK) class of proteins. We assume first that if no data on inhibitors are available for a target protein (JAK2), but a small numbers of inhibitors are known for homologous proteins (JAK1, JAK3 and TYK2). We show that the inclusion of relational domain-knowledge results in a potentially more effective generator of inhibitors than simple random sampling from the space of molecules or a generator without access to symbolic relations. The results suggest a way of combining symbolic domain-knowledge and deep generative models to constrain the exploration of the chemical space of molecules, when there is limited information on target-inhibitors. We also show how samples from the conditional generator can be used to identify potentially novel target inhibitors.

molecular biology↗